Fundamentals of Data Science and Statistics

Fundamentals of Data Science and Statistics

Rs.5,000.00

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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 handson 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

Live sessions over zoom everyday with immediate doubt clearing + Certificate for this course

Details:

Duration: 5 hours

Start Date: 1st June 2020, 7:00pm to 8:00pm

End Date: 5th June 2020, 7:00pm to 8:00pm

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

From : 1st June 2020, 7:00pm to 8:00pm  to 5th June 2020, 7:00pm to 8:00pm

No prior knowledge is required

You trainer has a PGDM from IIM Calcutta and has scored 100 percentile in CAT multiple times. She shall take the following lectures starting Monday 1st June over Zoom. Please let us know if you are interested and we shall send you the form.

*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

Note:
  1. Upto six weeks (or till submission of the final quiz) access to the course
  2. To get access to the certificate - you need to take the online exam at the end of the course
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