Program Overview

A career in analytics is an intellectually engaging experience with a challenging opportunity to work on some of the most exciting and innovative projects that shape the modern world. A bright mind with sound decision making skills integrated with a fair exposure to the tools and techniques of data analysis, machine learning and artificial intelligence would be critical to embrace lucrative market opportunities in areas like Business Analytics, Machine Learning and Artificial Intelligence. Data Analytics Foundation Program is specially designed to give the participants, an immersive experience to gain a quick and complete overview of all the essential skills that the industry requires in prospective analytics professionals.

Course Coverage

Tools – Gain a strong exposure to all tools like R, Python, Hadoop, Spark, Tableau and Power BI.

Data Analysis – Master data handling on both structured and unstructured data using SQL & NoSQL.

ML & AI – Implement all major algorithms in Machine Learning and Artificial Intelligence with confidence.

Big Data – Learn to use traditional data analytics in high performance Big-data ecosystems.

Certification – One attempt to a US based certification on Data Analytics [additional USD 250]

Convenience – A 60 hour program spread over 10 weekends with option to attend online / in-class

Job Ready – The entire training would focus on key technical requirements of analytics roles.

Course Ouline

Module 1: Applied Math and Stat for Analytics
S. No Topic Time (hrs)
1 Essential Probability 1
2 Descriptive Statistics 1
3 Hypothesis Testing 1
4 ANOVA 1
5 Differential Calculus 1
6 Linear Algebra 1
Module 2: Data Analysis with R
S. No Topic Time (hrs)
1 Data Types and Data Structures 1
2 Loops and Control Structures 1
3 Functions and Apply family 1
4 reshape2 and tidyr 1
5 dplyr 1
6 visualization with R 1
Module 3: Data Analysis with Python
S. No Topic Time (hrs)
1 Essential data structures 1
2 Loops 1
3 Functions 1
4 Numpy 1
5 Pandas 1
6 visualization with Python 1
Module 4: Data Analysis with MySQL
S. No Topic Time (hrs)
1 DDL and DML 1
2 Select queries 1
3 Advanced queries 1
4 Joins 1
5 Subqueries 1
6 Triggers and Stored Procedures 1
Module 5: Data Analysis with Tableau and Power BI
S. No Topic Time (hrs)
1 Power Query 1
2 Power Pivot 1
3 Dashboards 1
4 Introduction to Tableau 1
5 Dashboards  in Tableau 1
6 Storyboards 1

 

Module 6: Big Data Analysis
S. No Topic Time (hrs)
1 HDFS and MapReduce 1
2 Pig 1
3 Spark 1
4 Hive 1
5 MongoDB 1
6 Cassandra 1
Module 7: Supervised Learning
S. No Topic Time (hrs)
1 Simple Regression 1
2 Multiple Regression 1
3 Logistic Regression 1
4 KNN 1
5 Naïve Bayes 1
6 Decision Trees 1
Module 8: Unsupervised Learning
S. No Topic Time (hrs)
1 Hierarchical Clustering 1
2 K Means Clustering 1
3 Probability based Clustering 1
4 PCA 1
5 Factor Analysis 1
6 Association Rules Mining 1
Module 9 : Deep Learning
S. No Topic Time (hrs)
1 Introduction to Neural Networks 1
2 Gradient Descent 1
3 CNN 1
4 RNN 1
5 SOM 1
6 Boltzmann Machine 1
Module 10: Artificial Intelligence
S. No Topic Time (hrs)
1 Reinforcement Learning 1
2 Key Principles in AI 1
3 Markov Decision Processes 1
4 Dynamic Scenario Handling 1
5 Monte Carlo Analysis 1
6 Q Learning 1