• Describe the concept of data analysis and modeling with its usage.
• Know various visualization graphs
• Must know data Science workflow

Unit I: Introduction to Data Analysis & Modeling (3hrs)

1. Data Analysis, Data Modeling, and its importance.
2. Overview various Visualization graphs (scatter plot, histogram, line graph etc.)
3. Importance of Data Collection, Data Cleaning, Feature Engineering, Data Filtering, etc.

• Use regression analysis to predict the value of a dependent variable based on an independent variable.
• Interpret the meaning of regression coefficients.
• Evaluate the assumptions of regression analysis and know what to do if assumptions are violated.
• Make inferences about the slope and correlation coefficient.
• Generate excel output.
• Use excel output for solving problems.
• Analyze the relationship between one dependent variable and two or more independent variables and estimate the value of the dependent variable based on the values of the independent variables.
• Generate the excel output and interpret them

Unit II: Correlation and Regression (9 Hours)

1. Correlation: Introduction
2. Types of correlation: Scatter plot and Karlpearsons’ correlation coefficient.
3. Significance test of correlation coefficient.
4. Types of regression models
5. Determining the simple linear regression equation
   1. Visual exploration: exploring simple linear regression coefficients
   2. Predictions in regression analysis: interpolation versus extrapolation
   3. Computing the regression coefficients
6. Definition and Reasons for using multiple regression equation, Estimating multiple regression equation (2 independent variables)
7. Error Metrics: R Square, MSE, MAE, etc.
8. Hypothesis Testing (T-Test for the slope and co-relation coefficient). Confidence Interval estimation of the slope.

• Describe the various components of time Series.
• Describe the trend, cyclical, seasonal and irregular components of the time series model.
• Fit a linear trend equation to a time series.
• Smooth a time series with the moving average and exponential               smoothing techniques.
• Forecast the data by various techniques.
• Calculate and interpret measures of forecast accuracy

Unit III: Time Series Analysis and Forecasting (9 Hours)

1. Introduction of time series data, Components of time series analysis (Trend, Cyclical, Seasonal, Irregular) 
2. Trend analysis: Least square method, Second degree equation 
3. Forecasting Models: Naive, Moving average, Simple exponential smoothing model, linear model. 
4. Methods of measuring forecasting accuracy: MAD, MAPE, MSE, Cyclical Variation, Business cycle, Percent of trend, Relative cyclical residual, Seasonal Variation, Calculation of seasonal indices (Ratio to moving average), Deseasonalization.

• Explain the types of index number.
• Describe notion and terminology of index number.
• Introduce with the methods of constructing index number.
• Explain un-weighted and weighted method of index number.
• Test of consistency of index number.

Unit IV: Index Number (4 hours)

1. Definition and uses of Index Number
2. Types of Index Number
3. Notation and Terminology
4. Method of constructing Index Number
5. Un-weighted method
6. Weighted Method
7. Cost of living index number
8. Method of constructing cost of living Index numbers
   1. Aggregative expenditure method
   2. Family budget method

• Introduce linear programming (LPP).
• Explain the system of linear inequalities.
• Formulate LPP Model of the given theoretical problem.
• Identify the graphical solution of the LP Model.
• Familiar with the special cases in LP model.

Unit V: Linear Programming Problem (9 Hours)

1. Introduction, Decision variable, objective function, constraints, slack and surplus variable.
2. Model formulation for Linear Programming active constraints, inactive constraints, Alternative optimum solution for Linear Programming Problem, Sensitivity Analysis, Primal, Dual Problems.
3. Applications of Linear Programming
   • Transportation Model
   • Assignment Model

• Plot the network diagram of the given project.·         
• Identify critical path, critical and non-critical activities.
• Identify slack for non-critical activities.
• Calculate the associated probability.
• Plot time chart and identify scheduling flexibility.

Unit VI: Network Model (4 Hours)

1. Introduction, Activities, Events
2. Basic terminologies under project network
3. Network Construction (PERT/CPM)
4. Network Diagram
5. Probability in PERT Analysis

• Include one mini project of mentioned dataset with EDA.

Unit VII: Software tools and Data for Visualization (10 hrs)

Iris dataset, Titanic Dataset, California Housing Dataset, Mushroom Datasets, EDA and practical’s. With Python tools such as scikit-learn, seaborn/matplotlib.