|Tools||R, Python, Tableau, Notebook, Excel|
|Learning Mode||Classroom, online|
Who should do this course?
Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who are looking for building or switching their career to data science.
- Strong interest in data science.
- Good in maths.
- Background in intro level statistics.
- Python/Java programming experience or understanding of programming concepts such as variables, functions, loops, and basic python data structures like lists and dictionaries etc.
- Understand data and know how to extracts insights from it.
INTRODUCTION TO DATA SCIENCE
- What is data Science?
- Introduction. Importance of Data Science.
- Demand for Data Science Professional.
- Brief Introduction to Big data and Data Analytics. Lifecycle of data science.
- Tools and Technologies used in data Science.
- Business Intelligence vs Data Science.
- Role of a data scientist.
- R programming
- Python programming
- Spark programming
INTRODUCTION TO STATISTICS
- Fundamentals of Math and Probability
- Descriptive Statistics
- Inferential Statistics
- Hypothesis Testing
- Hands on with assignment & case studies
UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING
- Introduction to Machine Learning
- Linear Regression
- Logistic Regression
- Decision Trees and Supervised Learning
- Unsupervised Learning
- Introduction to Deep Learning
- Natural language Processing
APACHE SPARK ANALYTICS
- What is Spark
- Introduction to Spark RDD
- Introduction to Spark SQL and Dataframes using R-Spark for machine learning.
- Introduction to Tableau
- Connecting to data source
- Creating dashboard pages
- How to create calculated columns Different charts
Travel Flights Analysis (R)
Study the flights data along with their schedules, weather and also plane information thereby understanding flight routes and delays. This case study comprises of four sections (Import and understand the data, preparing data for analysis, perform exploratory analysis, forming & testing hypothesis etc)
Laptop Sales Analysis (R)
Data mining the sales transaction data using R to find key sales trends.
Banking Case study (R)
Understand customer spend & repayment behavior, along with evaluating areas of bankruptcy, fraud, and collections. Also, respond to customer requests for help with proactive offers and service.
Perform different graphical analysis (bar chart, pie chart, box plot, histogram, stacked charts, heat maps, scatter plots, panel charts etc) for solving various business problems
Credit Card Customers Segmentation (R)
A credit card company wishes to understand its customer behavior so to have an enriched customer profile by having intelligent KPI’s. The idea is to apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.
Proactive Attrition Management (R)
A wireless telecom companies wants to reduce customer churn by developing a proactive churn management model. The idea is to build a logistic regression based predictive model to develop an incentive plan for enticing would-be churners to remain with the company.
Predicting Loan Default (R/Python)
A bank would like to build credit risk model (application score card using PD models) to accept/ reject applications for loans. Also it wants to understand the key drivers for default or delinquency.
Key Drivers for Customer credit card spending (R)
The objective of this case study is to understand what’s driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow end-to-end model building process and help set the credit limit and designing new product offerings.
Time Series Forecasting (R)
Use time series analysis to forecast the outbound passenger movement for next few quarters.
Sentiment Analysis (R/Python)
Objective of this analysis is to obtain data from Twitter and check how the sentiment varies by country for a particular brand/keyword/company.