**Data Science and Data Analytics Using Spark | Scala | R | Python**

Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays

Real Time Projects , Assignments , scenarios are part of this course

Data Sets , installations , Interview Preparations , Repeat the session until 6 months are all attractions of this particular course

Trainer :- Experienced DataScience Consultant

Satisfied Learners

One time class room registraion to click here Fee 1000/-
## Clasroom training batch schedules:

## Online training batch schedules:

#### Exam Objectives

##### Configuration :-

##### Troubleshooting :-

##### High Availability :-

##### Data Ingestion – with Sqoop & Flume :-

##### Data Transformation Using Pig :-

##### Data Analysis Using Hive :-

##### Data Processing through Spark & Spark SQL& Python :-

##### Recomandtion Engine using Spark MLLIB & Python :-

##### Stream Data Processing using Spark Streaming& Python :-

##### Regression with Spark& Python :-

##### Classification with Spark & Python :-

##### Clustering with Spark & Python :-

##### Model Evaluation & Python :-

##### R Programming :-

Location | Day/Duration | Date | Time | Type | |
---|---|---|---|---|---|

Hinjewadi | Weekend | 23/11/2019 | 04:00 PM | New Batch | Enquiry |

Karve Road | Weekend | 23/11/2019 | 05:00 PM | Demo Batch | Enquiry |

Aundh | Weekday | 20/11/2019 | 10:00 AM | Demo Batch | Enquiry |

Hinjewadi | Weekend | 24/11/2019 | 04:00 PM | New Batch | Enquiry |

Mode | Day/Duration | Start Date | End Date | ₹ Price | Book Seat |
---|---|---|---|---|---|

Online | 8 Weeks, 4 Days | 16/02/2019 | 16/04/2019 | ₹ 20000.00 | Enroll Now |

**Data Science and Data Analytics Using Spark | R | Python**

**Learn Data Science, Deep Learning, & Machine Learning with Python & R Language With Live Machine Learning & Deep Learning Projects **

Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays

Real Time Projects , Assignments , scenarios are part of this course

Data Sets , installations , Interview Preparations , Repeat the session until 6 months are all attractions of this particular course

Trainer :- Experienced DataScience Consultant

**Want to be Future Data Scientist **

**Introduction: ** This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

**Data Science, Statistics with R & Python: **This course is an introduction to Data Science and Statistics using the R programming language with Python. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R and Python. If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

**What’s Spark? **If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

**Scala: **Scala is a general purpose programming language – like Java or C++. It’s functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark.

**Analytics: **Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.

**Machine Learning and Data Science : **Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

**Real life examples: **Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. ** **

**What am I going to get from this course?**

- Harness R and R packages to read, process and visualize data
- Understand linear regression and use it confidently to build models
- Understand the intricacies of all the different data structures in R
- Use Linear regression in R to overcome the difficulties of LINEST() in Excel
- Draw inferences from data and support them using tests of significance
- Use descriptive statistics to perform a quick study of some data and present results
- Use Spark for a variety of analytics and Machine Learning tasks
- Understand functional programming constructs in Scala
- Implement complex algorithms like PageRank or Music Recommendations
- Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
- Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX
- Write code in Scala REPL environments and build Scala applications with an IDE
- Course Completion Certificate.

**Target audience?**

- Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in Data Science Industry or want to be future Data Scientist.
- Engineers who want to use a distributed computing engine for batch or stream processing or both
- Analysts who want to leverage Spark for analyzing interesting datasets
- Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
- MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
- Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
- Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being modelers or data scientists
- Professionals who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis.

**Course Curriculum**

Data Science, Deep Learning, & Machine Learning with Python & R Language With Live Machine Learning & Deep Learning Projects

**Project 1**Build your own image recognition model with TensorFlow**Project 2**Predict fraud with data visualization & predictive modeling!**Project 3**Spam Detection**Project 4**Build your own Recommendation System**Project 5**Build your own Python predictive modeling, regression analysis & machine learning Model**Getting Started**- Course Introduction
- Course Material & Lab Setup
- Installation
- Python Basic – Part – 1
- Python Basic – Part – 2
- Advance Python – Part – 1
- Advance Python – Part – 2

**Statistics and Probability Refresher, and Python****Practice**- Types of Data
- Mean, Median, Mode
- Using mean, median, and mode in Python
- Variation and Standard Deviation
- Probability Density Function; Probability Mass Function
- Common Data Distributions
- Percentiles and Moments
- A Crash Course in matplotlib
- Covariance and Correlation
- Conditional Probability
- Exercise Solution: Conditional Probability of Purchase by Age
- Bayes’ Theorem

**Predictive Models**- Linear Regression
- Polynomial Regression
- Multivariate Regression, and Predicting Car Prices
- Multi-Level Models

**Machine Learning with Python**- Supervised vs. Unsupervised Learning, and Train/Test
- Using Train/Test to Prevent Overfitting a Polynomial Regression
- Bayesian Methods: Concepts
- Implementing a Spam Classifier with Naive Bayes
- K-Means Clustering
- Clustering people based on income and age
- Measuring Entropy
- Install GraphViz32. Decision Trees: Concepts
- Decision Trees: Predicting Hiring Decisions
- Ensemble Learning
- Support Vector Machines (SVM) Overview
- Using SVM to cluster people using scikit-learn

**Recommender Systems**- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- Finding Movie Similarities
- Improving the Results of Movie Similarities
- Making Movie Recommendations to People
- Improve the recommender’s results

**More Data Mining and Machine Learning Techniques**- K-Nearest-Neighbors: Concepts
- Using KNN to predict a rating for a movie
- Dimensionality Reduction; Principal Component Analysis
- PCA Example with the Iris data set
- Data Warehousing Overview: ETL and ELT
- Reinforcement Learning

**Dealing with Real-World Data**- Bias/Variance Tradeoff
- K-Fold Cross-Validation to avoid overfitting
- Data Cleaning and Normalization
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers

**Apache Spark: Machine Learning on Big Data**- Lab Set-up Warning & Error Handling
- Installing Spark – Part – 1
- Installing Spark – Part – 2
- Spark Introduction
- Spark and the Resilient Distributed Dataset (RDD)
- Introducing MLLib
- Decision Trees in Spark
- K-Means Clustering in Spark
- TF / IDF
- Searching Wikipedia with Spark
- Using the Spark 2.0 DataFrame API for MLLib

**Experimental Design**- A/B Testing Concepts
- T-Tests and P-Values
- Hands-on With T-Tests
- Determining How Long to Run an Experiment
- A/B Test Gotchas

**Deep Learning and Neural Networks**- Deep Learning Pre-Requisites
- The History of Artificial Neural Networks
- Deep Learning in the Tensorflow Playground
- Deep Learning Details
- Introducing Tensorflow
- Using Tensorflow, Part 1
- Using Tensorflow, Part 2
- Introducing Keras
- Using Keras to Predict Political Affiliations
- Convolutional Neural Networks (CNN’s)
- Using CNN’s for handwriting recognition
- Recurrent Neural Networks (RNN’s)
- Using a RNN for sentiment analysis
- The Ethics of Deep Learning
- Learning More about Deep Learning

**Statistics and Data Science in R****Introduction**- Introduction to R
- R and R studio Installation & Lab Setup
- Descriptive Statistics

**Descriptive Statistics**- Mean, Median, Mode
- Our first foray into R : Frequency Distributions
- Draw your first plot : A Histogram
- Computing Mean, Median, Mode in R
- What is IQR (Inter-quartile Range)?
- Box and Whisker Plots
- The Standard Deviation
- Computing IQR and Standard Deviation in R

**Inferential Statistics**- Drawing inferences from data
- Random Variables are ubiquitous
- The Normal Probability Distribution
- Sampling is like fishing
- Sample Statistics and Sampling Distributions

**Case studies in Inferential Statistics**- Case Study 1 : Football Players (Estimating Population Mean from a Sample)
- Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
- Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
- Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
- Case Study 5: A/B Testing (Comparing the means of two populations)
- Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)

**Diving into R**- Harnessing the power of R
- Assigning Variables
- Printing an output
- Numbers are of type numeric
- Characters and Dates
- Logicals

**Vectors**- Data Structures are the building blocks of R
- Creating a Vector
- The Mode of a Vector
- Vectors are Atomic
- Doing something with each element of a Vector
- Aggregating Vectors
- Operations between vectors of the same length
- Operations between vectors of different length
- Generating Sequences
- Using conditions with Vectors
- Find the lengths of multiple strings using Vectors
- Generate a complex sequence (using recycling)
- Vector Indexing (using numbers)
- Vector Indexing (using conditions)
- Vector Indexing (using names)

**Arrays**- Creating an Array
- Indexing an Array
- Operations between 2 Arrays
- Operations between an Array and a Vector
- Outer Products

**Matrices**- A Matrix is a 2-Dimensional Array
- Creating a Matrix
- Matrix Multiplication
- Merging Matrices
- Solving a set of linear equations

**Factors**- What is a factor?
- Find the distinct values in a dataset (using factors)
- Replace the levels of a factor
- Aggregate factors with table()
- Aggregate factors with tapply()

**Lists and Data Frames**- Introducing Lists
- Introducing Data Frames
- Reading Data from files
- Indexing a Data Frame
- Aggregating and Sorting a Data Frame
- Merging Data Frames

**Regression quantifies relationships between variables**- Linear Regression in Excel : Preparing the data.
- Linear Regression in Excel : Using LINEST()

**Linear Regression in R**- Linear Regression in R : Preparing the data
- Linear Regression in R : lm() and summary()
- Multiple Linear Regression
- Adding Categorical Variables to a linear mode
- Robust Regression in R : rlm()
- Parsing Regression Diagnostic Plots

**Data Visualization in R**- Data Visualization
- The plot() function in R
- Control color palettes with RColorbrewer
- Drawing bar plots
- Drawing a heatmap
- Drawing a Scatterplot Matrix
- Plot a line chart with ggplot

Summary

Reviewer

Kavitha Jadav

Review Date

Reviewed Item

I have just completed training for DataScience from Radical institute . Trainer has indepth knowledge and excellent teaching skill. Sir can identify the learning capacity of each student and trained from grass root. Sir starts training from very basic points and he gets all the prerequisites ready so no one is in trouble. He is very attentive. He always kept the sessions interesting and interactive, explain the concept with real time industry scenario. I just want to say that sir you are the best because you brought out the best in us. Highly recommend his Python training for the same. I also thank to Radical Technologies for keeping such good and experienced faculties through which we are getting high quality training.

Author Rating

DataQubez University creates meaningful big data & Data Science certifications that are recognized in the industry as a confident measure of qualified, capable big data experts. How do we accomplish that mission? DataQubez certifications are exclusively hands on, performance-based exams that require you to complete a set of tasks. Demonstrate your expertise with the most sought-after technical skills. Big data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At DataQubez, we’re drawing on our industry leadership and early corpus of real-world experience to address the big data & Data Science talent gap.

**How To Become Certified Data Science Professional Engineer**

Certification Code – DQCP – 501

Certification Description – DataQubez Certified Professional Data Science Engineer

Define and deploy a rack topology script, Change the configuration of a service using Apache Hadoop, Configure the Capacity Scheduler, Create a home directory for a user and configure permissions, Configure the include and exclude DataNode files

Restart an Cluster service, View an application’s log file, Configure and manage alerts Troubleshoot a failed job

Configure NameNode, Configure ResourceManager, Copy data between two clusters, Create a snapshot of an HDFS directory, Recover a snapshot, Configure HiveServer2

Import data from a table in a relational database into HDFS, Import the results of a query from a relational database into HDFS, Import a table from a relational database into a new or existing Hive table, Insert or update data from HDFS into a table in a relational database, Given a Flume configuration file, start a Flume agent, Given a configured sink and source, configure a Flume memory channel with a specified capacity

Write and execute a Pig script, Load data into a Pig relation without a schema, Load data into a Pig relation with a schema, Load data from a Hive table into a Pig relation, Use Pig to transform data into a specified format, Transform data to match a given Hive schema, Group the data of one or more Pig relations, Use Pig to remove records with null values from a relation, Store the data from a Pig relation into a folder in HDFS, Store the data from a Pig relation into a Hive table, Sort the output of a Pig relation, Remove the duplicate tuples of a Pig relation, Specify the number of reduce tasks for a Pig MapReduce job, Join two datasets using Pig, Perform a replicated join using Pig

Write and execute a Hive query, Define a Hive-managed table, Define a Hive external table, Define a partitioned Hive table, Define a bucketed Hive table, Define a Hive table from a select query, Define a Hive table that uses the ORCFile format, Create a new ORCFile table from the data in an existing non-ORCFile Hive table, Specify the storage format of a Hive table Specify the delimiter of a Hive table, Load data into a Hive table from a local directory Load data into a Hive table from an HDFS directory, Load data into a Hive table as the result of a query, Load a compressed data file into a Hive table, Update a row in a Hive table, Delete a row from a Hive table, Insert a new row into a Hive table, Join two Hive tables, Set a Hadoop or Hive configuration property from within a Hive query.

Frame big data analysis problems as Apache Spark scripts, Optimize Spark jobs through partitioning, caching, and other techniques, Develop distributed code using the Scala programming language, Build, deploy, and run Spark scripts on Hadoop clusters, Transform structured data using SparkSQL and DataFrames

Using MLLib to Produce Recomandation Engine, Run Page rank algorithem, using dataframes with mllib, Machine Learning with Spark

Process Stream Data using spark streaming.

Introduction to Linear Regression, Introduction to Regression Section, Linear Regression Documentation Alternate Linear Regression Data CSV File, Linear Regression Walkthrough , Linear Regression Project

Classification, Classification Documentation, Spark Classification – Logistic Regression , Logistic Regression Amendments, Classification Project

Clustering with Spark & Python, KMeans, Example of KMeans with Spark & Python, Clustering Project

Model Evaluation, Spark Model Evaluation, Spark – Model Evaluation – Regression

Program in R, Create Data Visualizations, Use R to manipulate data easily, Use R for Data Science, Use R for Data Analysis, Use R to handle csv,excel,SQL files or web scraping, Use R for Machine Learning Algorithms, Machine Learning with R – Linear Regression, Machine Learning with R – Logistic Regression

For Exam Registration of DataQubez Certified Professional Data Science Engineer, Click here:

Trainer for Big data & Data science course is having 11 years of exp. in the same technologies, he is industry expert. Trainer itself cloudera certified along with AWS (Solution Architecture) and GCP (Google Cloud Platform) certified. And also he is certified data scientist from The University of Chicago.

- Training By 11+ Years experienced Real Time Trainer
- A pool of 200+ real time Practical Sessions on Data Science and Analytics
- Scenarios and Assignments to make sure you compete with current Industry standards
- World class training methods
- Training until the candidate get placed
- Certification and Placement Support until you get certified and placed
- All training in reasonable cost
- 10000+ Satisfied candidates
- 5000+ Placement Records
- Corporate and Online Training in reasonable Cost
- Complete End-to-End Project with Each Course
- World Class Lab Facility which facilitates I3 /I5 /I7 Servers and Cisco UCS Servers
- Covers Topics other than from Books which is required for the IT Industry
- Resume And Interview preparation with 100% Hands-on Practical sessions
- Doubt clearing sessions any time after the course
- Happy to help you any time after the course

In classroom we solve real time problem, and also push students to create at-least a demo model and push his/her code into GIT, also in class we solve real time problem or data world problems.

Radical technologies, we believe that the best way to learn job-skills is from industry professionals. So, we are building an alternate higher education system, when you can learn job-skills from industry experts and get certified by companies. we complete the course as in classroom method with 85% Practical scenarios complete hands-on on each and every point of the course. and if student faces any issue in future he/she can join also in next batch. These courses are delivered through a live interactive classroom platform

We provide in classroom for solving real time problem, and also trying push to students at least create a demo model and push his/her code into GIT, also in class we solve real time Kaggle problem or data world problems.

Big Data with Cloud Computing (AWS) – Amazon Web Services

Big Data with Cloud Computing (GCP) – Google Cloud Platform

Big Data & Data Science with Cloud Computing (AWS) – Amazon Web Services

Big Data & Data Science with Cloud Computing (GCP) – Google Cloud Platform

Data Science with R & Spark with Python & Scala

Machine Learning with Google Cloud Platform with Tensor Flow

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Summary

Reviewer

Kavitha Jadav

Review Date

Reviewed Item

I have just completed training for DataScience from Radical institute . Trainer has indepth knowledge and excellent teaching skill. Sir can identify the learning capacity of each student and trained from grass root. Sir starts training from very basic points and he gets all the prerequisites ready so no one is in trouble. He is very attentive. He always kept the sessions interesting and interactive, explain the concept with real time industry scenario. I just want to say that sir you are the best because you brought out the best in us. Highly recommend his Python training for the same. I also thank to Radical Technologies for keeping such good and experienced faculties through which we are getting high quality training.

Author Rating