Data science for decision making – Online Bootcamp

Data Science for Decision Making

Become a Data Smart Manager

3 Weeks Live Sessions | 4th Weekend Campus Immersion

Scaling statistical algorithms to data sets and understanding the underlying rationale behind the same is one of the most critical pieces in the Data Science puzzle. The core skill sets required for Data Analysis problem definition, selecting the right statistical model, making the right assumptions and inferring the correct conclusions are emphasized in this bootcamp. This bootcamp will provide the necessary foundation to start your Data Science career.

Overview of Applied and Inferential Statistics

Ability to understand the underlying principles behind data analysis using R programming

Models and Application

Choosing and using appropriate statistical techniques to deliver insights into relevant organizational processes, and support decision making

Programming Skills

Get familiar with Programming in R for Statistical Analysis

Become Data Science Ready

Acquire the necessary knowledge and logic behind the important techniques in Data Science

Why this Bootcamp

Work on Real-life Data Science Problems

Take your career head-on by working on projects using a competency based learning paradigm. Quality of time spent and the outcome is far more important than the quantity.

Work 1:1 with a Mentor

We pair you with a mentor who has extensive professional and academic knowledge of the field. You will have one-on-one conversations with your mentor, and receive constructive feedback on your work.

We Will Keep You Engaged

Our mentors are here to keep you motivated, answer questions, provide feedback, and help deepen your understanding of essential tools and techniques. Learn with live online classes and face to face sessions. Learning is best when you are able to ask the questions and clarify your doubts with the faculty.

What You Will Learn

■ Measures of Central Tendency
■ Probability & Probability Distributions

■ Sampling Distributions, Estimation
■ Hypothesis Testing (t, Chi-Square, F Sampling Distributions)
■ ANOVA, Statistical Significance

■ Correlation and Simple Linear Regression
■ Multiple Linear Regression
■ Quantile and Logistic Regression

■ Stochastic Processes
■ Autoregressive-Moving Average Models ARMA
■ Box-Jenkins Model
■ Bayesian Inference and Regression

■ Case based analyses of data

1. Predict the number of items that a consumer will purchase.
2. Use Logistic Regression to predict fraudulent credit card transactions.
3. Analyze the relationship between wait times of callers and number of complaints in a call center.

Ability to be learn hands on with real industry data and delivering insights to industry jury is the best part of the program. Data Science and its application for Decision Science with practitioner faculty is the biggest highlight of the program. Strongly Recommend it.

Vinod Tiwari

Senior Analyst, TCS

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