Data scientists are one of the most demanding job profiles to thrive this century. Data Science and Machine Learning is everywhere; if you want to know how companies like Google, LinkedIn, Amazon, and even Facebook extract meaning insights from massive data sets, this data science course will impart you the fundamental as well as in depth practical knowledge you need.
Data Scientists are one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s very interesting work too!
If you’ve got some programming or scripting experience, this course will teach you the techniques and methods used by real data scientists and machine learning practitioners in the tech industry and will prepare you to get into the Tech Industry.
The 24 major projects and one big capstone project will help you to create a competitive Data Scientist portfolio!
Why to Choose Data Science as a Career?
What will you get?
- Python Basics
- Data Structures
- Flow Control
- Functions & Modules
- Data Science Introduction
- 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
- Terminologies of Statistics
- Measures of Centers
- Measures of Spread
- 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
- 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 Fuzzy C-means Clustering?
- What is Hierarchical Clustering?
- Introduction to Recommender System
- Types of Recommendations
- User-Based Recommendation
- Item-Based Recommendation
- Diﬀerence: User-Based and Item-Based Recommendation
- Recommendation Use Cases
- 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
It is preferable for candidates to have knowledge of different domains but at the same time, one should have specialization in some domain. Here you will get an opportunity to opt for specialisation out of Supply Chain, Ecommerce, BFSI and Tele Communications to master s specific domain.
Candidates will be given a chance to take part in two Hackathons which helps to understand how to apply complete course learning and solve real time problems.
- 360 Degree Support for Placements and Career.
- Mock Interviews Conducted by Data Scientists and Industry Leaders
- Guidance on Aptitude, Coding, Communication Skills, Presentation Skills, Grooming and Portfolio Building.
- Unlimited Support on Exercises, Projects, Portfolios, Resumes, and Interviews.
- Peer Group, and Personal Social Support for Motivation and Interaction.
- Personalized Projects for every student, Expert Reviews, Using Latest Techniques.