Learn Data Science, Deep Learning, & Machine Learning with Python / R /SAS With Live Machine Learning & Deep Learning Projects
Duration of Data Science Training : 80 hrs
Batch type : weekdays /weekends
Mode of Data Science Training: Classroom / Online / Corporate Training
Data Science Training , Real Time Projects , Assignments , scenarios are part of this course
Preparing you to become a Certified Data Scientist & Complete Placement Support for getting the job.
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
Data Science Training 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 Python / R / SAS : This course is an introduction to Data Science and Statistics using the R programming language OR Python OR SAS. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R / Python/ SaS. 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.
Data Science 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 Data frames 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.
Data Science 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.
Data Science 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 data scientists
Professionals who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis.
DATASCIENCE & MACHINE LEARNING WITH PYTHON
Data Science Course Content
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
Extract and analyze the data from twitter using Spark streaming
Comparison of Spark and Storm – Overview
Overview of GraphX module in spark
Creating graphs with GraphX
Introduction To Machine Learning Using Spark
Understand Machine learning framework
Implement some of the ML algorithms using Spark MLLib
Consolidate all the learnings
Working on Big Data Project by integrating various key components
Random password generator
CLI based scientific calculator
Site connectivity checker
Lawn Tennis Match Highlight (Can be extended to any sport)
Deep Learning Projects
Churn Modelling using ANN
Image classification using Transfer learning
Sentence Classification using RNN,LSTM,GRU
Sentence Classification using word embeddings
Object Detection using yolo
Machine Learning Projects
EDA on movies database
House price prediction using Regression
Predict survival on the Titanic using Classification
Twitter US Airline Sentiment
Restaurant revenue prediction
Note: Depends upon Trainers above projects may vary
DataScience Demo Session : –
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
High Availability :-
Configure NameNode, Configure ResourceManager, Copy data between two clusters, Create a snapshot of an HDFS directory, Recover a snapshot, Configure HiveServer2
Data Ingestion – with Sqoop & Flume :-
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
Data Transformation Using Pig :-
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
Data Analysis Using Hive :-
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.
Data Processing through Spark & Spark SQL& Python :-
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
Recomandtion Engine using Spark MLLIB & Python :-
Using MLLib to Produce Recomandation Engine, Run Page rank algorithem, using dataframes with mllib, Machine Learning with Spark
Stream Data Processing using Spark Streaming& Python :-
Process Stream Data using spark streaming.
Regression with Spark& Python :-
Introduction to Linear Regression, Introduction to Regression Section, Linear Regression Documentation Alternate Linear Regression Data CSV File, Linear Regression Walkthrough , Linear Regression Project
Clustering with Spark & Python, KMeans, Example of KMeans with Spark & Python, Clustering Project
Model Evaluation & Python :-
Model Evaluation, Spark Model Evaluation, Spark – Model Evaluation – Regression
R Programming :-
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