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**PG DIPLOMA CERTIFICATION IN DATASCIENCE & AI**

This program consists of Highly Practical learning of Statistics, Data Science, AI , Python, R, Big data Science ,Apache Spark & Scala, TensorFlow , Tableau SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM),K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R ,Pandas, NumPy, Matplotlib, Spark RDD, Spark SQL, Spark MLlib and Spark Streaming ,Scala Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka etc.

50 Projects – 8 Major and 42 Mini Projects | 500 Hrs of Training Content | 300+ Assignments | 20+ Use Case Studies

Gain Knowledge of 3+ Year Experienced Data Scientist

1 Global Certification is Free along with this course . All materials are giving free along with this course . Master program is integrated with International PG Certificate Program from UK . After 6 months of successful completion of Masters Program , You will start getting interview calls . Once you placed in Organization , You should complete the Assessments and remaining projects on weekends or on Online Mode .

Certificates Achieved : You will receive 3 certifications after successful completion .

Global Certification – Google Certified Data Engineer . RCITP after 6 months and International PG Diploma in Data Science and AI – UK After 1 year of successful completion of the course . You have to undergo multiple projects and assignments to achieve the International PG Diploma. After successful completion of the course, there will be 12 online assessments on topics covered during the course .

Major courses are Statistics, Data Science, AI , Python, R, Big data Science , Apache Spark & Scala, TensorFlow , Tableau SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM),K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R ,Pandas, NumPy, Matplotlib, Spark RDD, Spark SQL,

Spark MLlib and Spark Streaming ,Scala Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka etc.30 Plus tools are covered as part of this training

Avg. salary of a Data Scientists is goes to 20Lakhs per annum

Our Highlights

18000+ Students Empowered till now

Online/Classroom/Self-Paced

12 months Intensive Training

Triple Certification – Global Certification + RCITP After 6

months of Successful Completion of Masters + International PG

Diploma From UK after 1 Year

Start Date – Every Month One Batch ( Per Batch 25 Students )

400+ Hiring Partners

Certified from OTHM–UK Recognised by ofqual.gov.uk

Minimum 5 Interview Call and until you get Job

What you get After the Training – Gain Knowledge of At least 3+ Year Experienced Professional

**About the Program**

Project Driven industry mentorship, dedicated career support, learn 30 +tools Related to Data Science & AI.

Expertise in Data Science & AI with multiple assignments and Project. Dedicated Trainers with ample of Industry Experience . Project based IT Training and Certification programs .

Provide Level 7 Certification , which is equal to Master’s program in the rest of the world . Word Wide recognised certification from UK.Candidate and educational institutions can verify the certification online .

Program Overview – Key Highlights

Designed for Freshers to Working Professionals

Eligible for Ofqual regulates qualifications – UK Certified and World Recognised programs

30+ Data Science & AI tools

50+ Industry Projects , 300 + Live assignments , 150+ coding Solutions.

8 Major Projects , 42 Minor Projects & Use Case Studies , Job Oriented Scenarios

Ofqual – UK validated PG Diploma from UK

360 Degree Career Support

One-on-One with Industry Mentors

Dedicated Student Mentor

Placements with Top Firms

No Cost EMI Option – 0% EMI Option available

**Top Skills You Will Learn**

Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data, Natural Language Processing , Statistics, Data Science, AI , Python, R, Apache Spark & Scala, TensorFlow , Tableau SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM),K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R ,Pandas, NumPy, Matplotlib, Spark RDD, Spark SQL, Spark MLlib and Spark Streaming Scala Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka , Big data Science

**Job Opportunities**

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer,

Decision Scientist, Python Developer etc

Who Is This Program For?

Audience :- Freshers , Any Graduate , 2 to 4 Years Experienced up skilling enthusiasts . 3 rd Year Graduates who are going to attend campus Interviews . These courses designed in a way to be suitable for all branches of Engineering and all type of graduates – Science and non- science Graduates. Option to customise the subject according to the interest of candidate is also available.

**Minimum Eligibility**

Any Bachelor’s degree. Completed or not completed . No coding experience required . If you have any Educational Gap or any other career gap , you can do this program to boost up your career . The qualifications provided by UK Regulatory board is equal to Level 7 Masters Degree in UK.

Programming Languages and Tools Covered .

Post Graduate Diploma without quitting your job

**How You Benefit From This Program**

Post Graduate Diploma without Spending full time in College

3 Aditional Certifications / Qualification to Tag with your Academic Degree – Global

Certification in Data Engineering , RCITP- Radical Certified IT Professional and International

PG Diploma Certificate in Data Science & AI

Eligible for Ofqual regulates qualifications – UK Certified and World Recognised programs

Level 7 Program recognised world-wide for your Higher studies

Get recognised by High Value world recognised UK equal Master Degree

Career transition with up to 70% average salary hike

**Frequently Asked Questions**

**1. What is the eligibility Criteria ?**

Any One who is interested in statistics and programming . Those who drop out , Looking for Higher studies in UK and any other foreign countries

**2. Is this is a Job Guaranteed program**

Yes we give guaranteed interview calls until you find the Job . Minimum 5 interview calls and maximum until you get the satisfied Job .

**3 . What is RCITP ?**

Radical certified IT Professional – After 6 month of completion of the training, you will be

awarded masters Certificate from Radical – Masters in Data Science & AI – RCITP

**4.whether Master’s program is integrated with International PG Certificate program ?**

Yes it is. After 6 Month of enrolling for the masters course , and successfully completing the RCITP program ,You needs to undergo more projects and Assessments to obtain the PG Program . By default , all Master programs are integrated with International PG Certificate Program . By default , it will be converted into International PG certificate Program.

**5 . Selection procedure**

Once you enrolled for the course . Within one month , you have to complete the fee . If you

need any loan facility , This should be informed earlier before enrolling to make necessary

arrangements . Enrolment process of PG Diploma program will be starting after 1 month .

Necessary documents should be provided to enrol UK PG Program .

**Curriculum**

1. Statistics For Data Science – Using Python

Mode Of Training :- Classroom/ Online

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm. You will learn the importance of Python in real time environment and will be able to develop applications based on Object Oriented Programming concept. End of this course, you will be able to develop networking applications with suitable GUI

1.1Understanding the Data

Goal: In this module, you will be introduced to data and its types and accordingly

sample data and derive meaningful information from the data in terms different

statistical parameters.

Objectives: At the end of this Module, you should be able to:

Understand various data types

Learn Various variable types

List the uses of variable types

Explain Population and Sample

Discuss sampling techniques

Understand Data representation

Topics:

Introduction to Data Types

Numerical parameters to represent data

Mean

Mode

Median

Sensitivity

Information Gain

Page 8

Entropy

Statistical parameters to represent data

Hands-On/Demo

Estimating mean, median and mode using python

Calculating Information Gain and Entropy

1.2 Probability and its uses

Goal: In this module, you should learn about probability, interpret & solve real-life

problems using probability. You will get to know the power of probability with

Bayesian Inference.

Objectives: At the end of this Module, you should be able to:

Understand rules of probability

Learn about dependent and independent events

Implement conditional, marginal and joint probability using Bayes Theorem

Discuss probability distribution

Explain Central Limit Theorem

Topics:

Uses of probability

Need of probability

Bayesian Inference

Density Concepts

Normal Distribution Curve

Hands-On/Demo:

Calculating probability using python

Conditional, Joint and Marginal Probability using Python

Plotting a Normal distribution curve

Page 9

1.3 Statistical Inference

Goal: Draw inferences from present data and construct predictive models using

different inferential parameters (as a constraint).

Objectives: At the end of this Module, you should be able to:

Understand the concept of point estimation using confidence margin

Draw meaningful inferences using margin of error

Explore hypothesis testing and its different levels

Topics:

Point Estimation

Confidence Margin

Hypothesis Testing

Levels of Hypothesis Testing

Hands-On/Demo:

Calculating and generalizing point estimates using python

Estimation of Confidence Intervals and Margin of Error

1.4 Testing the Data

Goal: In this module, you should learn the different methods of testing the

alternative hypothesis.

Objectives: At the end of this module, you should be able to:

Understand Parametric and Non-parametric Testing

Learn various types of parametric testing

Discuss experimental designing

Explain a/b testing

Topics:

Parametric Test

Page 10

Parametric Test Types

Non- Parametric Test

Experimental Designing

A/B testing

Hands-On/Demo:

Perform p test and t tests in python

A/B testing in python

1.5 Data Clustering

Goal: Get an introduction to Clustering as part of this Module which forms the

basis for machine learning.

Objectives: At the end of this module, you should be able to:

Understand the concept of association and dependence

Explain causation and correlation

Learn the concept of covariance

Discuss Simpson’s paradox

Illustrate Clustering Techniques

Topics:

Association and Dependence

Causation and Correlation

Covariance

Simpson’s Paradox

Clustering Techniques

Hands-On/Demo:

Correlation and Covariance in python

Hierarchical clustering in python

K means clustering in python

Page 11

1.6 Regression Modelling

Goal: Learn the roots of Regression Modelling using statistics.

Objectives: At the end of this module, you should be able to:

Understand the concept of Linear Regression

Explain Logistic Regression

Implement WOE

Differentiate between heteroscedasticity and homoscedasticity

Learn the concept of residual analysis

Topics:

Logistic and Regression Techniques

Problem of Collinearity

WOE and IV

Residual Analysis

Heteroscedasticity

Homoscedasticity

Hands-On/Demo:

Perform Linear and Logistic Regression in python

Analyze the residuals using python

2. Statistics for Data Science – Using R

2.1 Understanding the Data

Page 12

Goal: In this module, you will be introduced to data and its types and will

accordingly sample data and derive meaningful information from the data in terms

of different statistical parameters.

Objectives: At the end of this Module, you should be able to:

Understand various data types

Learn Various variable types

List the uses of Variable types

Explain Population and Sample

Discuss Sampling techniques

Understand Data representation

Topics:

Introduction to Data Types

Numerical parameters to represent data

Mean

Mode

Median

Sensitivity

Information Gain

Entropy

Statistical parameters to represent data

Hands-On/Demo:

Estimating mean, median and mode using R

Calculating Information Gain and Entropy

2.2 Probability and its Uses

Goal: In this module, you will learn about probability, interpret & solve real-life

problems using probability. You will get to know the power of probability with

Bayesian Inference.

Objectives: At the end of this Module, you should be able to:

Page 13

Understand rules of probability

Learn about dependent and independent events

Implement conditional, marginal and joint probability using Bayes Theorem

Discuss probability distribution

Explain Central Limit Theorem

Topics:

Uses of probability

Need of probability

Bayesian Inference

Density Concepts

Normal Distribution Curve

Hands-On/Demo:

Calculating probability using R

Conditional, Joint and Marginal Probability using R

Plotting a Normal distribution curve

2.3 Statistical Inference

Goal: In this module, you will be able to draw inferences from present data and

construct predictive models using different inferential parameters (as the

constraint).

Objectives: At the end of this Module, you should be able to:

Understand the concept of point estimation using confidence margin

Demonstrate the use of Level of Confidence and Confidence Margin

Draw meaningful inferences using margin of error

Explore hypothesis testing and its different levels

Topics:

Point Estimation

Confidence Margin

Page 14

Hypothesis Testing

Levels of Hypothesis Testing

Hands-On/Demo:

Calculating and generalizing point estimates using R

Estimation of Confidence Intervals and Margin of Error

2.4 Testing the Data

Goal: In this module, you will learn the different methods of testing the alternative

hypothesis.

Objectives: At the end of this module, you should be able to:

Understand Parametric and Non-Parametric testing

Learn various types of Parametric testing

Explain A/B testing

Topics:

Parametric Test

Parametric Test Types

Non- Parametric Test

A/B testing

Hands-On/Demo:

Perform P test and T tests in R

2.5 Data Clustering

Goal: In this module, you will get an introduction to Clustering which forms the

basis for machine learning.

Objectives: At the end of this module, you should be able to:

Understand the concept of Association and Dependence

Page 15

Explain Causation and Correlation

Learn the concept of Covariance

Discuss Simpson’s paradox

Illustrate Clustering Techniques

Topics:

Association and Dependence

Causation and Correlation

Covariance

Simpson’s Paradox

Clustering Techniques

Hands-On/Demo:

Correlation and Covariance in R

Hierarchical clustering in R

K means clustering in R

2.6 Regression Modelling

Goal: In this module, you will be able to learn about the roots of Regression

Modelling using statistics.

Objectives: At the end of this module, you should be able to:

Understand the concept of Linear Regression

Explain Logistic Regression

Implement WOE

Differentiate between heteroscedasticity and homoscedasticity

Learn concept of residual analysis

Topics:

Logistic and Regression Techniques

Problem of Collinearity

WOE and IV

Page 16

Residual Analysis

Heteroscedasticity

Homoscedasticity

Hands-On/Demo:

Perform Linear and Logistic Regression in R

Analyze the residuals using R

Calculation of WOE values using R

3. DATASCIENCE & MACHINE LEARNING WITH

PYTHON

3.1 Introduction to Data Science with Python

What is analytics & Data Science?

Common Terms in Analytics

Analytics vs. Data warehousing, OLAP, MIS Reporting

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics?

Critical success drivers

Overview of analytics tools & their popularity

Analytics Methodology & problem solving framework

List of steps in Analytics projects

Identify the most appropriate solution design for the given problem statement

Project plan for Analytics project & key milestones based on effort estimates

Build Resource plan for analytics project

3.2 Python Essentials

Page 17

Why Python for data science?

Overview of Python- Starting with Python

Introduction to installation of Python

Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)

Understand Jupyter notebook & Customize Settings

Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)

Installing & loading Packages & Name Spaces

Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)

List and Dictionary Comprehensions

Variable & Value Labels – Date & Time Values

Basic Operations – Mathematical – string – date

Reading and writing data

Simple plotting

Control flow & conditional statements

Debugging & Code profiling

How to create class and modules and how to call them?

Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

3.3 Accessing/Importing And Exporting Data Using Python Modules

Importing Data from various sources (Csv, txt, excel, access etc)

Database Input (Connecting to database)

Viewing Data objects – subsetting Data, methods

Exporting Data to various formats

Important python modules: Pandas, beautiful soup

3.4 Data Manipulation – Cleansing – Munging using python modules

Cleansing Data with Python

Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables,

sampling, Data type conversions, renaming, formatting etc)

Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)

Python Built-in Functions (Text, numeric, date, utility functions)

Python User Defined Functions

Stripping out extraneous information

Normalizing data

Formatting data

Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

3.5 Data Analysis – Visualization Using Python

Introduction exploratory data analysis

Page 18

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats

etc)

3.6 Introduction to Statistics

Basic Statistics – Measures of Central Tendencies and Variance

Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests( One

sample, independent, paired), Analysis of variance, Correlations and Chi-square

Important modules for statistical methods: NumPy, SciPy, Pandas

3.7 Introduction to Predictive Modelling

Concept of model in analytics and how it is used?

Common terminology used in analytics & Modelling process

Popular modelling algorithms

Types of Business problems – Mapping of Techniques

Different Phases of Predictive Modelling

3.8 Data Exploration For Modelling

Need for structured exploratory data

EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

Identify missing data

Identify outliers data

Visualize the data trends and patterns

3.9 Data Preparation

Need of Data preparation

Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable

Reduction

Variable Reduction Techniques – Factor & PCA Analysis

3.10 Segmentation: Solving Segmentation Problems

Introduction to Segmentation

Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

Behavioural Segmentation Techniques (K-Means Cluster Analysis)

Page 19

Cluster evaluation and profiling – Identify cluster characteristics

Interpretation of results – Implementation on new data

3.11 Linear Regression: Solving Regression Problems

Introduction – Applications

Assumptions of Linear Regression

Building Linear Regression Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis

,etc)

Assess the overall effectiveness of the model

Validation of Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)

Interpretation of Results – Business Validation – Implementation on new data

3.12 Logistic Regression : Solving Classification Problems

Introduction – Applications

Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

Building Logistic Regression Model (Binary Logistic Model)

Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini,

KS, Misclassification, ROC Curve etc)

Validation of Logistic Regression Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation,

Drivers or variable importance, etc)

Interpretation of Results – Business Validation – Implementation on new data

3.13 Time Series Forecasting : Solving Forecasting Problems

Introduction – Applications

Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

Classification of Techniques(Pattern based – Pattern less)

Basic Techniques – Averages, Smoothening, etc

Advanced Techniques – AR Models, ARIMA, etc

Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

3.14 Machine Learning : Predictive Modelling

Introduction to Machine Learning & Predictive Modelling

Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs.

Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Page 20

Feature engineering & dimension reduction

Concept of optimization & cost function

Overview of gradient descent algorithm

Overview of Cross validation(Bootstrapping, K-Fold validation etc)

Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall,

precision, sensitivity, specificity, confusion metrics )

3.15 Unsupervised Learning : Segmentation

What is segmentation & Role of ML in Segmentation?

Concept of Distance and related math background

K-Means Clustering

Expectation Maximization

Hierarchical Clustering

Spectral Clustering (DBSCAN)

Principle component Analysis (PCA)

3.16 Supervised Learning :- Decision Trees

Decision Trees – Introduction – Applications

Types of Decision Tree Algorithms

Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-

Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees

Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other

Measures of Randomness

Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules

Decision Trees – Validation

Overfitting – Best Practices to avoid

3.17 Supervised Learning :- Ensemble Learning

Concept of Ensembling

Manual Ensembling Vs. Automated Ensembling

Methods of Ensembling (Stacking, Mixture of Experts)

Bagging (Logic, Practical Applications)

Random forest (Logic, Practical Applications)

Boosting (Logic, Practical Applications)

Ada Boost

Gradient Boosting Machines (GBM)

XGBoost

3.18 Supervised Learning :- Artificial Neural Network – ANN

Motivation for Neural Networks and Its Applications

Page 21

Perceptron and Single Layer Neural Network, and Hand Calculations

Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques

Neural Networks for Regression

Neural Networks for Classification

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating ANN models

3.19 Supervised Learning :- Support Vector Machines

Motivation for Support Vector Machine & Applications

Support Vector Regression

Support vector classifier (Linear & Non-Linear)

Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating SVM models

3.20 Supervised Learning :-KNN

What is KNN & Applications?

KNN for missing treatment

KNN For solving regression problems

KNN for solving classification problems

Validating KNN model

Model fine tuning with hyper parameters

3.21 Supervised Learning :- Naive Bayes

Concept of Conditional Probability

Bayes Theorem and Its Applications

Naïve Bayes for classification

Applications of Naïve Bayes in Classifications

3.22 Text Mining And Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties

of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence

Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)

Finding patterns in text: text mining, text as a graph

Natural Language processing (NLP)

Text Analytics – Sentiment Analysis using Python

Text Analytics – Word cloud analysis using Python

Text Analytics – Segmentation using K-Means/Hierarchical Clustering

Text Analytics – Classification (Spam/Not spam)

Applications of Social Media Analytics

Page 22

Metrics(Measures Actions) in social media analytics

Examples & Actionable Insights using Social Media Analytics

Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

4 . DATASCIENCE WITH R

4.1 Introduction to Data Science With R

What is analytics & Data Science?

Common Terms in Analytics

Analytics vs. Data warehousing, OLAP, MIS Reporting

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics?

Critical success drivers

Overview of analytics tools & their popularity

Analytics Methodology & problem solving framework

List of steps in Analytics projects

Identify the most appropriate solution design for the given problem statement

Project plan for Analytics project & key milestones based on effort estimates

Build Resource plan for analytics project

Why R for data science?

4.2 Data Importing / Exporting

Introduction R/R-Studio – GUI

Concept of Packages – Useful Packages (Base & Other packages)

Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)

Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)

Database Input (Connecting to database)

Exporting Data to various formats)

Viewing Data (Viewing partial data and full data)

Variable & Value Labels – Date Values

4.3 Data Manipulation

Data Manipulation steps

Creating New Variables (calculations & Binning)

Dummy variable creation

Applying transformations

Handling duplicates

Page 23

Handling missings

Sorting and Filtering

Subsetting (Rows/Columns)

Appending (Row appending/column appending)

Merging/Joining (Left, right, inner, full, outer etc)

Data type conversions

Renaming

Formatting

Reshaping data

Sampling

Data manipulation tools

Operators

Functions

Packages

Control Structures (if, if else)

Loops (Conditional, iterative loops, apply functions)

Arrays

R Built-in Functions (Text, Numeric, Date, utility)

Numerical Functions

Text Functions

Date Functions

Utilities Functions

R User Defined Functions

R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

4.4 Data Analysis – Visualization

ntroduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)

R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)

R Packages for Graphical Analysis (base, ggplot, lattice,etc)

4.5 Introduction To Statistics

Basic Statistics – Measures of Central Tendencies and Variance

Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling – Concept of Hypothesis Testing

Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

4.6 Predictive Modelling

Page 24

Concept of model in analytics and how it is used?

Common terminology used in analytics & modelling process

Popular modelling algorithms

Types of Business problems – Mapping of Techniques

Different Phases of Predictive Modelling

4.7 Data Exploration For Modeling

4.8Data Preparation

Need of Data preparation

Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable

Reduction

Variable Reduction Techniques – Factor & PCA Analysis

4.9 Segmentation: Solving Segmentation Problems

Introduction to Segmentation

Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

Behavioral Segmentation Techniques (K-Means Cluster Analysis)

Cluster evaluation and profiling – Identify cluster characteristics

Interpretation of results – Implementation on new data

4.10 Linear Regression: Solving Regression Problems

Introduction – Applications

Assumptions of Linear Regression

Building Linear Regression Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis

,etc)

Assess the overall effectiveness of the model

Validation of Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)

Interpretation of Results – Business Validation – Implementation on new data

4.11 Logistic Regression: Solving Classification Problems

Introduction – Applications

Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

Building Logistic Regression Model (Binary Logistic Model)

Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini,

KS, Misclassification, ROC Curve etc)

Validation of Logistic Regression Models (Re running Vs. Scoring)

Page 25

Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation,

Drivers or variable importance, etc)

Interpretation of Results – Business Validation – Implementation on new data

4.12 Time Series Forecasting: Solving Forecasting Problems

Introduction – Applications

Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

Classification of Techniques(Pattern based – Pattern less)

Basic Techniques – Averages, Smoothening, etc

Advanced Techniques – AR Models, ARIMA, etc

Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

4.13 Machine Learning -Predictive Modeling – Basics

Introduction to Machine Learning & Predictive Modeling

Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs.

Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Feature engineering & dimension reduction

Concept of optimization & cost function

Overview of gradient descent algorithm

Overview of Cross validation(Bootstrapping, K-Fold validation etc)

Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision,

sensitivity, specificity, confusion metrics )

4.14 Unsupervised Learning: Segmentation

What is segmentation & Role of ML in Segmentation?

Concept of Distance and related math background

K-Means Clustering

Expectation Maximization

Hierarchical Clustering

Spectral Clustering (DBSCAN)

Principle component Analysis (PCA)

4.15 Supervised Learning: Decision Trees

Decision Trees – Introduction – Applications

Types of Decision Tree Algorithms

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Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-

Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees

Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other

Measures of Randomness

Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules

Decision Trees – Validation

Overfitting – Best Practices to avoid

4.16 Supervised Learning: Ensemble Learning

Concept of Ensembling

Manual Ensembling Vs. Automated Ensembling

Methods of Ensembling (Stacking, Mixture of Experts)

Bagging (Logic, Practical Applications)

Random forest (Logic, Practical Applications)

Boosting (Logic, Practical Applications)

Ada Boost

Gradient Boosting Machines (GBM)

XGBoost

4.17 Supervised Learning: Artificial Neural Networks (ANN)

Motivation for Neural Networks and Its Applications

Perceptron and Single Layer Neural Network, and Hand Calculations

Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques

Neural Networks for Regression

Neural Networks for Classification

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating ANN models

4.18 Supervised Learning: Support Vector Machines

Motivation for Support Vector Machine & Applications

Support Vector Regression

Support vector classifier (Linear & Non-Linear)

Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating SVM models

4.19 Supervised Learning: KNN

What is KNN & Applications?

KNN for missing treatment

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KNN For solving regression problems

KNN for solving classification problems

Validating KNN model

Model fine tuning with hyper parameters

4.20 Supervised Learning: Naïve Bayes

Concept of Conditional Probability

Bayes Theorem and Its Applications

Naïve Bayes for classification

Applications of Naïve Bayes in Classifications

4.21 Text Mining & Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties

of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence

Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)

Finding patterns in text: text mining, text as a graph

Natural Language processing (NLP)

Text Analytics – Sentiment Analysis using R

Text Analytics – Word cloud analysis using R

Text Analytics – Segmentation using K-Means/Hierarchical Clustering

Text Analytics – Classification (Spam/Not spam)

Applications of Social Media Analytics

Metrics(Measures Actions) in social media analytics

Examples & Actionable Insights using Social Media Analytics

Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

5 . AI With ML & DL

5.1 Introduction with Artificial Intelligence.

What is AI (Artificial Intelligence) ?

What types of intelligences we are talking about?

Different definitions and Ultimate goal of AI.

What are application areas for AI?

History of AI and some real life examples of AI.

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5.2 ML and other related terms to AI.

What is ML and How it is related with AI?

What is NLP and How it is related with AI?

What is DL and How it is related with ML and AI?

What are ANNs and DNNs and How are they related to AI?

5.3 A working example of AI and ML.

Project 1 – These simple tasks are to make you understand how AI and ML can find their applications

in real life.

5.4 Python libraries for ML.

What are Libraries, packages and Modules?

What are top Python libraries for ML in Python?

5.5 Setting up Anaconda development environment.

Why choosing Anaconda development environment?

Setting up Anaconda development environment on Windows 10 PC.

Verifying proper installation of Anaconda environment.

5.6 Getting into core development of ML.

What is a classifier in ML?

Important elements and flow of any ML projects.

Let’s develop our first ML program – explanations

Let’s develop our first ML program – development

Project – 2

These simple tasks are going to give you some great experience with Machine Learning introductory

programs or better say, “Hello world” programs of Machine Learning.

5.7 Different ML techniques.

What all ML techniques are there?

Evaluation methods of all ML techniques.

(IRIS flower project) Developing complete project of ML.

Developing complete ML project – understanding data set

Developing complete ML project – understanding flow of project

Developing complete ML project – visualizing data set through Python

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Developing complete ML project – development

Developing complete ML project – concepts explanations

–(Digit recognition project) Developing another project of ML.

Project 3

After completing these project, you have done and understood multiple complete projects of Machine

Learning.

5.8 Introduction of Ai with Deep Learning

Installation

CPU Software Requirements

CPU Installation of PyTorch

PyTorch with GPU on AWS

PyTorch with GPU on Linux

PyTorch with GPU on MacOSX

5.9 PyTorch Fundamentals: Matrices

Matrix Basics

Seed for Reproducibility

Torch to NumPy Bridge

NumPy to Torch Bridge

GPU and CPU Toggling

Basic Mathematical Tensor Operations

Summary of Matrices

5.10 PyTorch Fundamentals: Variables and Gradients

Variables

Gradients

Summary of Variables and Gradients

5.11 Linear Regression with PyTorch

Linear Regression Introduction

Linear Regression in PyTorch

Linear Regression From CPU to GPU in PyTorch

Summary of Linear Regression

5.12 Logistic Regression with PyTorch

Logistic Regression Introduction

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Linear Regression Problems

Logistic Regression In-depth

Logistic Regression with PyTorch

Logistic Regression From CPU to GPU in PyTorch

Summary of Logistic Regression

5.13 Feedforward Neural Network with PyTorch

Logistic Regression Transition to Feedforward Neural Network

Non-linearity

Feedforward Neural Network in PyTorch

More Feedforward Neural Network Models in PyTorch

Feedforward Neural Network From CPU to GPU in PyTorch

Summary of Feedforward Neural Network

5.14 Convolutional Neural Network (CNN) with PyTorch

Feedforward Neural Network Transition to CNN

One Convolutional Layer, Input Depth of 1

One Convolutional Layer, Input Depth of 3

One Convolutional Layer Summary

Multiple Convolutional Layers Overview

Pooling Layers

Padding for Convolutional Layers

Output Size Calculation

CNN in PyTorch

More CNN Models in PyTorch

CNN Models Summary

Expanding Model’s Capacity

CNN From CPU to GPU in PyTorch

Summary of CNN

5.15 Recurrent Neural Networks (RNN)

Introduction to RNN

RNN in PyTorch

More RNN Models in PyTorch

RNN From CPU to GPU in PyTorch

Summary of RNN

5.16 Long Short-Term Memory Networks (LSTM)

Introduction to LSTMs

LSTM Equations

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LSTM in PyTorch

More LSTM Models in PyTorch

LSTM From CPU to GPU in PyTorch

Summary of LSTM

6 Apache Spark and Scala

6.1 Introduction to Big Data Hadoop and Spark

Learning Objectives: Understand Big Data and its components such as HDFS. You

will learn about the Hadoop Cluster Architecture and you will also get an

introduction to Spark and you will get to know about the difference between batch

processing and real-time processing.

Topics:

What is Big Data?

Big Data Customer Scenarios

Limitations and Solutions of Existing Data Analytics Architecture with Uber Use

Case

How Hadoop Solves the Big Data Problem?

What is Hadoop?

Hadoop’s Key Characteristics

Hadoop Ecosystem and HDFS

Hadoop Core Components

Rack Awareness and Block Replication

YARN and its Advantage

Hadoop Cluster and its Architecture

Hadoop: Different Cluster Modes

Big Data Analytics with Batch & Real-time Processing

Why Spark is needed?

What is Spark?

How Spark differs from other frameworks?

Spark at Yahoo!

6.2 Introduction to Scala for Apache Spark

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Learning Objectives: Learn the basics of Scala that are required for programming

Spark applications. You will also learn about the basic constructs of Scala such as

variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists,

and many more.

Topics:

What is Scala?

Why Scala for Spark?

Scala in other Frameworks

Introduction to Scala REPL

Basic Scala Operations

Variable Types in Scala

Control Structures in Scala

Foreach loop, Functions and Procedures

Collections in Scala- Array

ArrayBuffer, Map, Tuples, Lists, and more

Hands-on:

Scala REPL Detailed Demo

6.3 Functional Programming and OOPs Concepts in Scala

Learning Objectives: In this module, you will learn about object-oriented

programming and functional programming techniques in Scala.

Topics:

Functional Programming

Higher Order Functions

Anonymous Functions

Class in Scala

Getters and Setters

Custom Getters and Setters

Properties with only Getters

Auxiliary Constructor and Primary Constructor

Singletons

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Extending a Class

Overriding Methods

Traits as Interfaces and Layered Traits

Hands-on:

OOPs Concepts

Functional Programming

6.4 Deep Dive into Apache Spark Framework

Learning Objectives: Understand Apache Spark and learn how to develop Spark

applications. At the end, you will learn how to perform data ingestion using Sqoop.

Topics:

Spark’s Place in Hadoop Ecosystem

Spark Components & its Architecture

Spark Deployment Modes

Introduction to Spark Shell

Writing your first Spark Job Using SBT

Submitting Spark Job

Spark Web UI

Data Ingestion using Sqoop

Hands-on:

Building and Running Spark Application

Spark Application Web UI

Configuring Spark Properties

Data ingestion using Sqoop

6.5 Playing with Spark RDDs

Learning Objectives: Get an insight of Spark – RDDs and other RDD related

manipulations for implementing business logics (Transformations, Actions and

Functions performed on RDD).

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Topics:

Challenges in Existing Computing Methods

Probable Solution & How RDD Solves the Problem

What is RDD, It’s Operations, Transformations & Actions

Data Loading and Saving Through RDDs

Key-Value Pair RDDs

Other Pair RDDs, Two Pair RDDs

RDD Lineage

RDD Persistence

WordCount Program Using RDD Concepts

RDD Partitioning & How It Helps Achieve Parallelization

Passing Functions to Spark

Hands-on:

Loading data in RDDs

Saving data through RDDs

RDD Transformations

RDD Actions and Functions

RDD Partitions

WordCount through RDDs

6.6 Data Frames and Spark SQL

Learning Objectives: In this module, you will learn about SparkSQL which is used

to process structured data with SQL queries, data-frames and datasets in Spark

SQL along with different kind of SQL operations performed on the data-frames.

You will also learn about the Spark and Hive integration.

Topics:

Need for Spark SQL

What is Spark SQL?

Spark SQL Architecture

SQL Context in Spark SQL

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User Defined Functions

Data Frames & Datasets

Interoperating with RDDs

JSON and Parquet File Formats

Loading Data through Different Sources

Spark – Hive Integration

Hands-on:

Spark SQL – Creating Data Frames

Loading and Transforming Data through Different Sources

Stock Market Analysis

Spark-Hive Integration

6.7 Machine Learning using Spark MLlib

Learning Objectives: Learn why machine learning is needed, different Machine

Learning techniques/algorithms, and SparK MLlib.

Topics:

Why Machine Learning?

What is Machine Learning?

Where Machine Learning is Used?

Face Detection: USE CASE

Different Types of Machine Learning Techniques

Introduction to MLlib

Features of MLlib and MLlib Tools

Various ML algorithms supported by MLlib

6.8 Deep Dive into Spark MLlib

Learning Objectives: Implement various algorithms supported by MLlib such as

Linear Regression, Decision Tree, Random Forest and many more.

Topics:

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Supervised Learning – Linear Regression, Logistic Regression, Decision Tree,

Random Forest

Unsupervised Learning – K-Means Clustering & How It Works with MLlib

Analysis on US Election Data using MLlib (K-Means)

Hands-on:

Machine Learning MLlib

K- Means Clustering

Linear Regression

Logistic Regression

Decision Tree

Random Forest

6.9 Understanding Apache Kafka and Apache Flume

Learning Objectives: Understand Kafka and its Architecture. Also, learn about

Kafka Cluster, how to configure different types of Kafka Cluster. Get introduced to

Apache Flume, its architecture and how it is integrated with Apache Kafka for event

processing. At the end, learn how to ingest streaming data using flume.

Topics:

Need for Kafka

What is Kafka?

Core Concepts of Kafka

Kafka Architecture

Where is Kafka Used?

Understanding the Components of Kafka Cluster

Configuring Kafka Cluster

Kafka Producer and Consumer Java API

Need of Apache Flume

What is Apache Flume?

Basic Flume Architecture

Flume Sources

Flume Sinks

Flume Channels

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Flume Configuration

Integrating Apache Flume and Apache Kafka

Hands-on:

Configuring Single Node Single Broker Cluster

Configuring Single Node Multi Broker Cluster

Producing and consuming messages

Flume Commands

Setting up Flume Agent

Streaming Twitter Data into HDFS

6.10 Apache Spark Streaming – Processing Multiple Batches

Learning Objectives: Work on Spark streaming which is used to build scalable

fault-tolerant streaming applications. Also, learn about DStreams and various

Transformations performed on the streaming data. You will get to know about

commonly used streaming operators such as Sliding Window Operators and

Stateful Operators.

Topics:

Drawbacks in Existing Computing Methods

Why Streaming is Necessary?

What is Spark Streaming?

Spark Streaming Features

Spark Streaming Workflow

How Uber Uses Streaming Data

Streaming Context & DStreams

Transformations on DStreams

Describe Windowed Operators and Why it is Useful

Important Windowed Operators

Slice, Window and ReduceByWindow Operators

Stateful Operators

6.11 Apache Spark Streaming – Data Sources

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Learning Objectives: In this module, you will learn about the different streaming

data sources such as Kafka and flume. At the end of the module, you will be able to

create a spark streaming application.

Topics:

Apache Spark Streaming: Data Sources

Streaming Data Source Overview

Apache Flume and Apache Kafka Data Sources

Example: Using a Kafka Direct Data Source

Perform Twitter Sentimental Analysis Using Spark Streaming

Hands-on:

Different Streaming Data Sources

7 Data Visualization And Analytics – Tableau

Audience:

This course is designed to provide you with the skills required to become a

Tableau power user. The course is designed for the professional who has solid working

experience with Tableau and wants to take it to the next level. You should have a deep

understanding of all the fundamental concepts of building worksheets and dashboards, but

may scratch your head when working with more complex issues.

7.1 Learning Objectives: At the end of this class, the student will be able to:

Build advanced chart types and visualizations

Build complex calculations to manipulate your data

Work with statistics and statistical techniques

Work with parameters and input controls

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Implement advanced geographic mapping techniques and use custom images to build

spatial visualizations of non-geographic data

Implement all options in working with data: Joining multiple tables, data blending,

performance considerations and working with the Data Engine, and understand when to

implement which connection method.

Build better dashboards using techniques for guided analytics, interactive dashboard

design and visual best practices

Implement many efficiency tips and tricks

Understand the basics of Tableau Server and other options for sharing your results

7.2 Introduction and Getting Started

Why Tableau? Why Visualization?

The Tableau Product Line

Level Setting – Terminology

Getting Started – creating some powerful visualizations quickly

Review of some Key Fundamental Concepts

7.3 Filtering, Sorting & Grouping

Filtering, Sorting and Grouping are fundamental concepts

when working with and analyzing data. We will briefly review these topics as they apply to

Tableau

Advanced options for filtering and hiding

Understanding your many options for ordering and grouping your data: Sort, Groups, Bins,

Sets

Understanding how all of these options inter-relate

7.4 Working with Data– In the Advanced class, we will understand the difference between

joining and blending data, and when we should do each. We will also consider the implications

of working with large data sets, and consider options for when and how to work with extracts

and the data engine. We will also investigate best practices in “sharing” data sources for

Tableau Server users.

Data Types and Roles

Dimension versus Measures

Data Types

Discrete versus Continuous

The meaning of pill colors

Database Joins

Data Blending

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Working with the Data Engine / Extracts and scheduling extract updates

Working with Custom SQL

Adding to Context

Switching to Direct Connection

7.5 Working with Calculated Data and Statistics– In the Fundamentals Class, we were

introduced to some basic calculations: basic string and arithmetic calculations and ratios and

quick table calculations. In the Advanced class, we will extend those concepts to understand

the intricacies of manipulating data within Tableau

7.6 A Quick Review of Basic Calculations

Arithmetic Calculations

String Manipulation

Date Calculations

Quick Table Calculations

Custom Aggregations

Custom Calculated Fields

Logic and Conditional Calculations

Conditional Filters

7.7 Advanced Table Calculations

Understanding Scope and Direction

Calculate on Results of Table Calculations

Complex Calculations

Difference From Average

Discrete Aggregations

Index to Ratios

7.8 Working with Parameters– In the Fundamentals class, we were introduced to

parameters – How to create a parameter and use it in a calculation. In the Advanced class, we

will go into more details on how we can use parameters to modify our title, create What-If

analysis, etc

Parameter Basics

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Data types of parameters

Using parameters in calculated fields

Inputting parameter values and parameter control options

Advanced Usage of Parameters

Using parameters for titles, field selections, logic statements, Top X

7.9 Building Advanced Chart Types and Visualizations / Tips & Tricks– This topic

covers how to create some of the chart types and visualizations that may be less obvious in

Tableau. It also covers some of the more common tips & tricks / techniques that we use to

assist customers in solving some of their more complex problems.

Bar in Bar

Box Plot

Bullet Chart

Custom Shapes

Gantt Chart

Heat Map

Pareto Chart

Spark Line

KPI Chart

7.10 Best Practices in Formatting and Visualizing

Formatting Tips

Drag to Legend

Edit Legend

Highlighting

Labeling

Legends

Working with Nulls

Table Options

Annotations and Display Options

Introduction to Visualization Best Practices

7.11 Introduction to XL Data Handling

Introduction to Excel Environment

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Formatting and Conditional Formatting

Data Sorting, Filtering and Data Validation

Understanding Name Ranges

7.12 Data Manipulation Using Functions

Descriptive functions: sum, count, min, max, average, counta, countblank

Logical functions: IF, and, or, not

Relational operators > >= < <= = !=

Nesting of functions

Date and Time functions: today, now, month, year, day, weekday, networkdays, weeknum, time,

minute, hour

Text functions: left, right, mid, find, length, replace, substitute, trim, rank, rank.avg, upper, lower,

proper

Array functions: sumif, sumifs, countif, countifs, sumproduct

Use and application of lookup functions in excel: Vlookup, Hlookup

Limitations of lookup functions

Using Index, Match, Offset, concept of reverse vlookup

7.13 Data Analysis And Reporting

Data Analysis using Pivot Tables – use of row and column shelf, values and filters

Difference between data layering and cross tabulation, summary reports, advantages and limitations

Change aggregation types and summarisation

Creating groups and bins in pivot data

Concept of calculated fields, usage and limitations

Changing report layouts – Outline, compact and tabular forms

Show and hide grand totals and subtotals

Creating summary reports using pivot tables

7.14 Data Visualization In Excel

Overview of chart types – column and bar charts, line and area charts, pie charts, doughnut charts,

scatter plots

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How to select right chart for your data

Chart formatting

Creating and customizing advance charts – thermometer charts, waterfall charts, population

pyramids

7.15 Overview Of Dashboards

What is dashboard & Excel dashboard

Adding icons and images to dashboards

Making dashboards dynamic

7.16 Create Dashboards In Excel – Using Pivot Controls

Concept of pivot cache and its use in creating interactive dashboards in Excel

Pivot table design elements – concept of slicers and timelines

Designing sample dashboard using Pivot Controls

Design principles for including charts in dashboards – do's and don’t's

7.17 Business Dashboard Creation

Complete Management Dashboard for Sales & Services

Best practices – Tips and Tricks to enhance dashboard designing

7.18 SQL: Understanding RDBMS

Schema – Meta Data – ER Diagram

Looking at an example of Database design

Data Integrity Constraints & types of Relationships (Primary and foreign key)

Basic concepts – Queries, Data types & NULL Values, Operators and Comments in SQL

7.19 SQL: Utilising The Object Explorer

What is SQL – A Quick Introduction

Installing MS SQL Server for windows OS

Introduction to SQL Server Management Studio

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Understanding basic database concepts

7.20 SQL: Data Based Objects Creation (DDL Commands)

Creating, Modifying & Deleting Databases and Tables

Drop & Truncate statements – Uses & Differences

Alter Table & Alter Column statements

Import and Export wizard to get the data in SQL server from excel files or delimited files

7.21 SQL: Data Manipulation (DML Commands)

Insert, Update & Delete statements

Select statement – Subsetting, Filters, Sorting. Removing Duplicates, grouping and aggregations etc

Where, Group By, Order by & Having clauses

SQL Functions – Number, Text, Date, etc

SQL Keywords – Top, Distinct, Null, etc

SQL Operators – Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard

operators and wildcard characters, etc

7.22 SQL: Accessing Data From Multiple Tables Using SELECT

Append and JoinsUnion and Union All – Use & constraints

Intersect and Except statements

Table Joins – inner join, left join, right join, full join

Cross joins/cartesian products, self joins, natural joins etc

Inline views and sub-queries

Optimizing your work

7.23 Tableau: Getting Started

What is Tableau? What does the Tableau product suite comprise of? How Does Tableau Work?

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Tableau Architecture

Connecting to Data & Introduction to data source concepts

Understanding the Tableau workspace

Dimensions and Measures

Data Types & Default Properties

Tour of Shelves & Marks Card

Using Show Me

Saving and Sharing your work-overview

7.24 Tableau: Data Handling & Summaries

Date Aggregations and Date parts

Cross tab & Tabular charts

Totals & Subtotals

Bar Charts & Stacked Bars

Line Graphs with Date & Without Date

Tree maps

Scatter Plots

Individual Axes, Blended Axes, Dual Axes & Combination chart

Parts of Views

Sorting

Trend lines/ Forecasting

Reference Lines

Filters/Context filters

Sets

o In/Out Sets

o Combined Sets

Grouping

Bins/Histograms

Drilling up/down – drill through

Hierarchies

View data

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Actions (across sheets)

7.25 Tableau: Building Advanced Reports/ Maps

Explain latitude and longitude

Default location/Edit locations

Building geographical maps

Using Map layers

7.26 Tableau: Calculated Fields

Working with aggregate versus disaggregate data

Explain – #Number of Rows

Basic Functions (String, Date, Numbers etc)

Usage of Logical conditions

7.27 Tableau: Table Calculations

Explain scope and direction

Percent of Total, Running / Cumulative calculations

Introduction to LOD (Level of Detail) Expressions

User applications of Table calculations

7.28 Tableau: Parameters

Using Parameters in

o Calculated fieldsBins

o Reference Lines

o Filters/Sets

Display Options (Dynamic Dimension/Measure Selection)

Create What-If/ Scenario analysis

7.29 Tableau: Building Interactive Dashboards

Combining multiple visualizations into a dashboard (overview)

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Making your worksheet interactive by using actions

o Filter

o URL

o Highlight

Complete Interactive Dashboard for Sales & Services

7.30 Tableau: Formatting

Options in Formatting your Visualization

Working with Labels and Annotations

Effective Use of Titles and Captions

7.31 Tableau: Working With Data

Multiple Table Joins

Data Blending

Difference between joining and blending data, and when we should do each

Toggle between to Direct Connection and Extracts

7.32 MS VBA

Introducing VBA

What is Logic?

What Is VBA?

Introduction to Macro Recordings, IDE

How VBA Works with Excel

Working In the Visual Basic Editor

Introducing the Excel Object Model

Using the Excel Macro Recorder

VBA Sub and Function Procedures

Key Components of Programming language

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Essential VBA Language Elements

Keywords & Syntax

Programming statements

Variables & Data types

Comments

Operators

Working with Range Objects

A look at some commonly used code snippets

Programming constructs in VBA

Control Structures

Looping Structures

The With- End with Block

Functions & Procedures in VBA – Modularizing your programs

Worksheet & workbook functions

Automatic Procedures and Events

Arrays

Objects & Memory Management in VBA

The NEW and SET Key words

Destroying Objects – The Nothing Keyword

Error Handling

Controlling accessibility of your code – Access specifiers

Code Reusability – Adding references and components to your code

Communicating with Your Users

Simple Dialog Boxes

User Form Basics

Using User Form Controls

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Add-ins

Accessing Your Macros through the User Interface

Retrieve information through Excel from Access Database using VBA

Quick Enquiry