Basics of Data Science and Machine Learning with Python
Course code: DAPY01
Duration: 44 Hours
About this Course
This course is designed and delivered to a large extent with practice sessions. Data science goes beyond traditional analytics and statistical models and facilitates building predictive models. Participants will learn the concepts and motivation to apply data science and interdisciplinary programs. This also provides you the foundation skills to produce various types of predictive models for machine learning, the algorithms involved to build such models and how predictive modeling and machine learning are interrelated. The key aspect of this course is that, participants are exposed to sufficient amount of real time use cases, for each of the machine learning algorithms learnt, in order to analyze, conceptualize and build various types of predictive models, on their own.
Students and Working Professionals who want to learn Data Science, Machine Learning & Python. This is a foundation course in data science imparting necessary skills to begin working on machine learning projects.
Understanding of basic mathematics including probability and statistics, and analytical thinking are required.
This course provides the foundation knowledge needed in analytics & data science:
- Introduction to analytics and data science and their applications today
- Using Python libraries and extensions
- Basic mathematics
- Machine learning algorithms
- Predictive models
- Case study for applying each of machine learning algorithm learnt
1. Welcome to the Course - 4 hours
Applications of machine learning, why machine learning is the future; installing python and anaconda.
2. Python Basics - 2 hours
Introduction to python, first program on python, conditional statements, strings, lists, Dictionaries and modules, data frames introduction.
Description and configuration of basic packages.
- 2. Pandas
- 3. Matplotlib
3. Basic statistics - 2 hours
Explanation on statistical mathematics required for data science with Python examples. (Distributions, mean, mode, median, correlations etc.)
4. Types of machine learning - 4 hours
Various machine learning types explained. Concept of supervised, unsupervised and reinforcement machine learning and different algorithms are explained. Applicability (or) usage scenario of these models / concepts is explained. (Participants are expected to complete at least one case study based on each of the algorithms taught).
5. Making a predictive model - 8 hours
Making a predictive model using linear regression. Hands on python case study for a predictive model using machine learning algorithm with real data.
6. Cancer detection using logistic regression - 8 hours
Cancer detection using logistic regression. Identification of cancer with python using machine learning with real data.
7. Cluster analysis - 8 hours
Clustering of Customers of a company using K mean Algorithm and visualization of clusters
8. Decision Sciences - 8 hours
Decision Sciences Using Decision tree to make real business Decisions with Python