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
Regression Fundamentals The linear regression equation Linear Regression explained Linear Regression with independent variable Interpreting R -Squared Evaluating Model Performance Key assumptions of Linear Regression Residual Analysis 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 Quiz
Chapter 3 - Time Series Forecasting
Time Series Fundamentals Visualizing time series data using plots Components of Time series Stationary time series Forecasting fundamentals Forecasting techniques 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 Chapter Quiz
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 Quiz
Chapter 6 -Business Decisions I
Parametric & Non Parametric Methods – Model building Tradeoffs -Accuracy vs Explainability Chapter Quiz
Chapter 7 -Business Decisions ..II
Framework to choose the right model to address business problems Chapter Quiz
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
Institute of Product Leadership is Asia’s First Business School providing accredited degree programs and certification courses exclusively in Product Management and Marketing, Data Science and Technology Management.