About the course

Focuses on Statistics that constitutes the backbone for Data Science along with Data Science modeling for Classification and Regression. Concepts will be taught in a hands-on manner with data sets.

Munira Lokhandwala, CFA, FRM is  PGDM, IIM – Calcutta and has cracked 100%ile in CAT multiple times.

She has developed educational content that is used by clients such as ISB, Axis Bank, Castrol, etc.

And has Twenty plus years of training experience.

The curriculum covers the topics of MBA

Learning Outcomes

After completing this course, you will be able to:

• Understand the fundamentals of Data Science and Statistics.
• Convert your .txt file into .xlsx file.
• Predict values using linear regression.
• Work with frequency distribution, mean, covariance, serial correlation, multi-collinearity, conditional probability etc.
• Analyze data using Sampling Distribution, t-distribution, F-distribution, Chi-Square distribution etc.
• Boost your hireability through innovative and independent learning.

• Get a certificate on successful completion of the course.

Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science or Information Technology.

Teachers/Faculties: All computer science and engineering teachers/faculties.

Professionals: All IT professionals, who wish to acquire new skills or improve their existing skills.

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Details:

Curriculum:

*Lecture 1: Data Handling and Descriptive Statistics*

• Converting a .txt file into .xlsx
• Types of Data
• Central Tendency Measures
• Frequency Distribution
• Dispersion Measures
• Skewness and Mean
• Covariance and Correlation
• Scatterplots
• How to make sense of Data

*Lecture 2: Probability and Distributions*

• Importance of Probability for DS
• Revision of Probability Concepts
• Conditional Probability
• Dependent and Independent Events
• Bayes' Formula
• Uniform and Binomial Distribution
• Normal and Lognormal Distribution
• p values
• False Positive and False Negative

*Lecture 3: Sampling, Estimation and Hypothesis Testing*

• Sampling Distribution
• Central Limit Theorem
• Standard Error
• Confidence Intervals
• Sample Size Determination
• Hypothesis Testing Steps
• Type I and Type II Errors
• P-value Revisit
• Student's t- Distribution
• F Distribution
• Chi-Square Distribution
• All the above wrt Applicability and with Data Sets
• Sampling Biases

*Lecture 4: Linear Regression Fundamentals*

• Scatter Plot and Correlation
• Applicability of Linear Regression
• Dependent and Independent Variable
• Assumptions behind Linear Regression
• Linear Regression on Excel
• Interpret the slope and the intercept
• Calculations of predicted value
• Understand SEE, Coefficient of Determination, Confidence Intervals
• Significance of the Regression Model
• Anova Table Analysis
• Limitations of Regression Analysis

*Lecture 5: Linear Regression Advanced*

• Multiple Regression: Step-wise and Simultaneous Regression
• Adjusted Rsquared
• Anova Table Analysis
• Dummy Variables
• Heteroskedasticity
• Serial correlation
• Multi-collinearity
• Model Mis-specifications