Advance Data Science

Module 1: Data Science Introduction & Use Cases

Module 2: Python Basics: Basic Syntax, Data Structures

Module 3: Python Basics: Loops, If-elif statements, Functions, Exception Handling

Module 4: Statistics 1: Measures of central tendency, Population, Sample, Probability Distribution

Module 5: Statistics 1: Normal and Binomial Distribution, Random Variable, Pictorial Representations

Module 6: Python Advanced: Numpy, Pandas

Module 7: Python Advanced: Data Manipulation, Matplotlib

Module 8: Exploratory Data Analysis: Data Cleaning, Data Wrangling

Module 9: Exploratory Data Analysis: Data Visualisation

Module 10: Exploratory Data Analysis: Case Study

Module 1: Introduction to Tableau

Module 2: Data visualisation

Module 3: Analytics concepts with Statistics - I

Module 4: Analytics concepts with Statistics - II

Analytics Concepts using caluclated feilds

Module 6: Analytics concepts for integrating dashboards

Module 7: Mini project workshop - Visual Analytics

Module 8: Integration of Tableau with Python

Module 1: ML Introduction & Use Cases

Module 2: Statistics 2 - Inferential Statistics

Module 3: Linear Regression

Module 4: Logistic Regression

Module 5: Decision Trees, Random Forest

Module 6: Modelling Techniques (PCA, Feature Engineering)

Module 7: KNN, Naive Bayes

Module 8: Support Vector Machines(SVM)

Module 9: Clustering, K-means

Module 10: Time Series Modelling

All the courses are instructor-led and take place online. The online interface lets you and the faculty have a two-way interaction. It’s as good as sitting in a physical classroom.

All classes take place over the weekends in the mornings. There’ll be one class of 2 to 2.5 hours on Saturdays and Sundays each. This means that you can now acquire in-demand skills without compromising on your schedule.

Yes, the recordings of the trial classes are uploaded.

In physical classrooms, students generally feel hesitant to ask questions. If you miss any class or didn’t understand some concepts, you can’t go through the class again. However, in online courses, it’s possible to do that. We share the recordings of all our classes after each class with the student. Also, there’s no hassle of long distance commuting and disrupting your schedule.

We believe that unless you implement the concepts studied in the classes, you are unable to join the dots and hence can’t see the entire picture. Our capstone projects let you apply the learned concepts to real-world data sets. You’ll be working on real-time data-sets (which can run in 100s of MBs or possibly in GBs!) which you get to choose from a variety of domains such as retail, finance, social media, healthcare, etc. These datasets have been curated from top sources such as World Bank, US Health Department, Carnegie Mellon, Stanford and many more.

You’ll get access to our virtual computing lab through the login credentials provided to you. The virtual machines will enable you to work on “Big Data” sets for your projects and practice hands-on too.

We provide certification of completion to students who attend at least 70% of the classes. After the course ends, we conduct a certification exam that evaluates them on the skills they have learnt. Certification from Hatigen is provided to only those who clear the exam. The exam is purely case-based and not even a single theory question is asked.

We provide career workshops and industry immersion sessions to help you become ready for roles you are aspiring for. We also help you in resume review and interview preparation. If you diligently follow our advice, you should start getting interview calls as soon as you finish the course.