About Us

Data Science

Data Science is a vast technology that encompasses various aspects in many fields. Data Science also forms the basis for working with big data and analytics also. By creating a clear understanding in data science, one can discover many opportunities as more and more businesses are becoming data driven.

Data science course helps you learn how you can analyze data using automated methods, collating data from different devices using sophisticated techniques.

Data science can be applicable in many areas such as predictive and prescriptive analysis, machine learning etc. This data can be used for making critical business decisions that will have a larger impact.

Why Choose Affirmative?

Experience Counts

At affirmative, someone with prior experience on working in the concepts they teach are faculties. So, you can be sure that you are learning from someone who not only knows the technology but has hands-on experience in working with it. This way, apart from the textbook knowledge, you can get to know what kind of difficulties are faced by data scientists in real-life scenarios.

Put your knowledge into work

Apart from online training, one of the major areas of focus is also the real-time case studies. This is where you get to learn the nitty-gritty involved in applying your textbook knowledge to real time workplace conditions. Therefore, you can be up to date in terms of mastering the technology and be job-ready.

Multiple arenas to explore

Data scientists are most sought after not only in the field of information technology but also in many other field where large data is involved. Ecommerce, banking, entertainment is some of the verticals where data scientists help make critical business decisions. Forget about doing something you don’t like and get driven by passion. Be prepared to be chosen by some of the top players in the industry and pave your way to success in professional life.

Prerequisites for learning Data Science

Love for mathematics and basic knowledge in database concepts will help you in mastering this course.

Who is this program for?

Information architects, Big data analysts, Analytics professionals and anyone looking to make it big in the field of data science can sign up for this course.

Career Prospects

Data Scientist is the best job of the 21st century by Harvard Business Review. There are plenty of opportunities for people with right knowledge in data science. Affirmative can help you tap the right sources to get your career path setup. You are one step closer to your dream of establishing a great career.

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Monday - Friday

7:00 AM IST - 8:00 AM IST


Monday - Friday

7:00 AM IST - 8:00 AM IST

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  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark
  • Introduction to Machine Learning
  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution
  • Data Analysis Pipeline
  • What is Data Extraction?
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithm: Linear Regression and Logistic Regression
  • What are classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification
  • What is Clustering & its use cases
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendations
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation use cases
  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text
  • TF-IDF
  • Beyond TF-IDF
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN’s