Why Choose 6 Month Big Data Analytics Programme Program?
Real world datasets from companies like Nike, Yelp, Amazon, Netflix etc. are provided to our students
Telco customer churn analysis
Telco churn data consists of 21 variables on customer behavior with information on services and products
Amazon electronic product sales analysis
The Amazon reviews, Comments contains 142.8 million rows of review data of popular products
Zika virus outbreak Analysis
The Zika Virus dataset contains 100K + rows of data on countries affected worldwide
World development indicators
WDI data set consists of 20 variables on resource utilization by 217 countries between years 2000 - 2015
Amazon Fine Food Reviews
Amazon Fine Food dataset consists of 500k+ rows of data with 20 variables on food product reviews
Exclusive Interview preparation guides, Career Support, Placement Opportunity and many more..!
Our world-class faculty are experienced working professionals working in senior positions of top-tier companies
What are you going to learn ?
Module 1: Introduction to Big Data
Module 2: Python Essentials - I
Module 3: Python Essentials - II
Module 4: Introduction to Hadoop
Module 5: Hadoop Architecture & Ecosystem
Module 6: Hadoop Distributed File System(HDFS)
Module 7: Introduction to Map Reduce
Module 8: MapReduce Programming with Python
Module 9: Apache Pig
Module 10: Apache Pig Hands - on
Module 11: Apache Hive
Module 12: Apache Hive Hands - on
Module 1: Data Science Introduction & Use Cases
Module 2: Python Basics: Basic Syntax, Data Structures/p>
Module 3: Python Basics: Loops, If-elif statements, Functions, Exception Handling
Module 4: Statistics 1: Measures of central tendency, Population, Sample, Probability Distribution
Module 5: Statistics 1: Normal and Binomial Distribution, Random Variable, Pictorial Representations
Module 6: Python Advanced: Numpy, Pandas
Module 7: Python Advanced: Data Manipulation, Matplotlib
Module 8: Exploratory Data Analysis: Data Cleaning, Data Wrangling
Module 9: Exploratory Data Analysis: Data Visualisation
Module 10: Exploratory Data Analysis: Case Study
Module 1: Programming with Scala-I
Module 2: Programming with Scala-II
Module 3: Introduction to Apache Spark architecture
Module 4: RDD, transformations and actions
Module 5: Spark Programming
Module 6: Spark SQL
Module 7: Spark Streaming - I
Module 8: Spark Streaming - II
Module 9 Machine Learning with Spark MLlib
Module 1: ML Introduction & Use Cases
Module 2: Statistics 2 - Inferential Statistics
Module 3: Linear Regression
Module 4: Logistic Regression
Module 5: Decision Trees, Random Forest
Module 6: Modelling Techniques (PCA, Feature Engineering)
Module 7: KNN, Naive Bayes
Module 8: Support Vector Machines(SVM)
Module 9: Clustering, K-means
Module 10: Time Series Modelling
Module 1: Introduction to NoSQL
Module 2: Getting started with MongoDB
Module 3: MongoDB Essentials - I
Module 4: MongoDB Essentials - II
Module 5: Hands-on MongoDB
Module 6: HBase
Module 7: SQOOP
Module 8: FLUME
Module 1: Introduction to Tableau
Module 2: Data visualisation
Module 3: Introduction to Tableau
Module 4: Analytics concepts with Statistics - II
Module 5: Analytics concepts for integrating dashboards
All the courses are instructor-led and take place online. The online interface lets you and the faculty have a two-way interaction. It’s as good as sitting in a physical classroom.
All classes take place over the weekends in the mornings. There’ll be one class of 2 to 2.5 hours on Saturdays and Sundays each. This means that you can now acquire in-demand skills without compromising on your schedule.
Yes, the recordings of the trial classes are uploaded.
In physical classrooms, students generally feel hesitant to ask questions. If you miss any class or didn’t understand some concepts, you can’t go through the class again. However, in online courses, it’s possible to do that. We share the recordings of all our classes after each class with the student. Also, there’s no hassle of long distance commuting and disrupting your schedule.
We believe that unless you implement the concepts studied in the classes, you are unable to join the dots and hence can’t see the entire picture. Our capstone projects let you apply the learned concepts to real-world data sets. You’ll be working on real-time data-sets (which can run in 100s of MBs or possibly in GBs!) which you get to choose from a variety of domains such as retail, finance, social media, healthcare, etc. These datasets have been curated from top sources such as World Bank, US Health Department, Carnegie Mellon, Stanford and many more.
You’ll get access to our virtual computing lab through the login credentials provided to you. The virtual machines will enable you to work on “Big Data” sets for your projects and practice hands-on too.
We provide certification of completion to students who attend at least 70% of the classes. After the course ends, we conduct a certification exam that evaluates them on the skills they have learnt. Certification from Tech Mahindra is provided to only those who clear the exam. The exam is purely case-based and not even a single theory question is asked.
We provide career workshops and industry immersion sessions to help you become ready for roles you are aspiring for. We also help you in resume review and interview preparation. If you diligently follow our advice, you should start getting interview calls as soon as you finish the course.