This course aims to prepare participants for a career in Data Science and Machine Learning. You will start by learning Python, the most popular language for Data Science. You will then develop skills for Data Analysis and Data Visualization and also get a practical introduction in Machine Learning. Finally you will apply and demonstrate your knowledge of Data Science and Machine Learning with a Capstone Project involving a real life business problem.
Data Visualization in Python
In this course, you will be guided through the setup of Matplotlib- A plotting library for Python programming language. You will learn various types of visual representations like pie charts, histograms, scatter plots and many more. This course has been specifically outlined for individuals are interested in learning the various ways of displaying data visually. At the end of this course, you will be able to create live graphs and visualize geographical data on maps.
Discovering Statistics Using SPSS
This course introduces participants to the logic and use of statistical techniques in social sciences. The will be able to evaluate social or business research problems themselves and to have a capability of deciding on the true statistical alternative after handling the business problems. It presents the basics of the Statistical Package for the Social Sciences (SPSS). It introduces the SPSS Windows environment, discusses how to create a dataset, variable transformations, data manipulations, and descriptive statistics. It will assist trainees in developing the data analysis skills necessary for autonomous and efficient computer processing, manipulation, and analysis of empirical data in the study of social science data or business data.
Discovering Statistics Using STATA
This course shows how to program and employ the powerful statistical commands available in STATA package by recalling some statistical concepts. The course include but not limited to, introducing STATA windows environment. It discusses how to create a dataset, importing data from other sources, data manipulations, descriptive statistics and regression analysis. It will assist trainees in developing the data analysis skills necessary for autonomous and efficient computer processing, manipulation, and analysis of empirical data various fields such as social science and business.
Data Mining for Decision Making
This course explores supervised and unsupervised machine learning and cover standard data mining techniques using machine learning algorithms. Your learning will include correlation and association, discriminant analysis, naïve Bayes, nearest neighbor, cluster analysis, decision trees, and neural networks. Text mining is also covered.
Ethics in Data Analytics
This course examines the next generation of business analytics and opportunities to use data for the greater good. Participants will explore issues such as social marketing, fraud, risk management, mobile intelligence, human capital, and data privacy.