Amazing course for even beginners in the field of Predictive analytics. Highly recommend to do this course for enhancing the analytical skills. Examples and case studies explain the concepts very well.
I have learned a lot from the course. Many interesting topics that help me to understand this field of Analytics. This course has generated more interest for me to continue learning more in this specialization
What will you Learn?
Understand the difference between Cross sectional and Longitudinal data.
Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.
Understand Parametric and Non Parametric modelling approach towards addressing the key tradeoff between Predictive accuracy and Explain- ability of models.
Use LPP towards building multiple “What if “ scenarios which are widely used in business decision making.
Conceptualize Gradient Descent Algorithm which is a key foundation for most of the widely used Machine learning algorithms to be introduced subsequently.
Top skills you will learn
Develop predictive and prescriptive models using numerical data
Optimization through Linear Programming
Gradient Descent and it’s applicability in Machine Learning
Understanding cross sectional and longitudinal data
Chapter 2 - Simple Linear Regression and Multiple Linear Regression
The linear regression equation
Linear Regression explained
Linear Regression with independent variable
Interpreting R -Squared
Evaluating Model Performance
Key assumptions of Linear Regression
Statistical tests to validate assumptions
Correlation and Casuation
Heat map and Scatter plots
Multiple Linear Regression use case
Interpreting regression outputs
Regression use cases
Chapter 3 - Time Series Forecasting
Time Series Fundamentals
Visualizing time series data using plots
Components of Time series
Stationary time series
Forecasting techniques : Exponential Smoothing
Forecasting techniques : Holt’s method
Forecasting techniques : Holt’s Winter method
Forecasting techniques : ACF & PACF
Forecasting techniques : ARIMA
Forecasting techniques : ARIMA models in Python
Applications of Time Series
Linear Programming fundamentals
Components of LPP
Formulating the LPP model
Solving linear models-Graphical method
Solving linear models -Simplex method
Assumptions of LPP
Business applications of LPP
Chapter 6 -Business Decisions I
Parametric & Non Parametric Methods – Model building
Tradeoffs -Accuracy vs Explainability
Chapter 7 -Business Decisions ..II
Framework to choose the right model to address business problems
1 – 8 yrs work experience.- Engineering, Math/Statistics/Programming background preferred Typical roles: Domain experts, Engineers, Software and IT Professionals, Project Managers, Business Analysts, Consultants, Entrepreneurs.
Engineers with over 5 years of experience
Common Scenarios to Enroll
Data-Centric MindsetYou need to make informed decisions by collaborating with teams who analyze data to obtain the best information and derive best insights possible
Innovation DriverRecognizing the importance of innovation at work, you want to learn how to generate ideas and drive their execution
34% of the firms that were top in class in using analytics got
about 6% more profitability and were about 5% more productive