Data Science Course in Bangalore with Placement

Category: Academics

1:Introduction to Python Programming Language Introduction and Installation of Python software Python packages: Pandas, & Numpy Concepts of Data frame Filtering Loc and iloc for filtering Usage of Boolean in Filtering Appending 2: Data handling in Python Handling of Missing values If else statement Extra trick of using if else statement Removal of Duplicates Frequency Distribution Merging – Inner, Outer, Left and Right Binding and Appending Descriptive Statistics Inbuilt Numeric functions of R 3: More data handling using Python Pivot Table of Excel in Python Grouping function Learning of SQL queries using Python Grouping numeric data 4: Additional functions of Python Text functions Data cleaning with efficient text functions Inbuilt String functions of Python Reshape functions of Python 5: Statistic Everything you want to know about statistics….Well sort of!! Mean, Median, Mode Standard Deviation, Variance, Normal Distribution Hypothesis testing T-test, Anova, Normality test 6: Linear Regression Predictive Analytics – Linear Regression Concepts of Linear Regression Simple and Multiple Linear Regression Automatic Dummy Variables creation technique Model Validation parameters Model Assumption testing Splitting of data for Validation and testing Business Case Study with real data to model in Python 7: Linear Regression Practice Case Study Participants will be asked to develop a Linear Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client. 8: Logistic Regression Predictive Analytics – Logistic Regression Concepts of Logistic Regression Difference between Linear Regression and Logistic Regression Automatic Dummy Variables creation technique Model Validation parameters Model Assumption testing Splitting of data for Validation and testing Business Case Study with real data to model in Python 9: Logistic Regression Practice Case Study Participants will be asked to develop a Logistic Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client. 10:Time Series Forecasting Time series forecasting: ARIMA Difference between forecasting and prediction Concepts of time series data Concepts of ARIMA Descriptive analytics for ARIMA Development of model Best model selection Forecasting with the best model Residual analysis Business Case Study with real data to model in R software Participants will be asked to develop a model in presence of the instructor.