About Course
Currently, there’s exponential growth in the Indian analytics industry. Many IT enterprises and tech-based start-up companies are actively hiring skilled Data Scientists who can help them get their economies back on the track. As per the experts view, India would soon become the hub for data analytics job roles and at the same time, the Indian analytics market is expected to reach 6 million dollars in 2025. More than 3 lakh new job opportunities are being generated every year in this domain.
Enterprises across the industry verticals like IT, Healthcare, Pharmaceutical, E-commerce, Banking, Insurance, Media, and Retail are among the prominent industries where there's an exclusive demand for Data Scientists. With salary packages ranging as high as $143, 500 to $180, 500, job roles in Data Science have become a lucrative career option for both fresher and working professionals.
Course Curriculum
Introduction to Data Science
- What is data science?
- How is data science different from BI and Reporting?
- What is difference between AI, Data Science, Machine Learning, Deep Learning
- Job Land scape and Preparation Time
- Who are data scientists?
- What skillsets are required?
- What is day to day job of Data Scientist
- What kind of projects they work on?
- End to End Data Science Project Life Cycle
- Data Science roles – functions, pay across domains, experience
Business Statistics
- Data types
- Continuous variables
- Ordinal Variables
- Categorical variables
- Time Series
- Miscellaneous
- Common Data Science Terminology
- Descriptive statistics
- Basics concepts of probability
- Frequentist versus Bayesian Probability
- Axioms of probability theory,
- Permutations and combinations
Introduction to R
- A Primer to R programming
- What is R? Similarities to OOP and SQL
- Types of objects in R – lists, matrices, arrays, data.frames etc
- Creating new variables or updating existing variables
- If statements and conditional loops - For, while etc.
- String manipulations
Python for Data Science
- Understanding the reason of Python’s popularity
- Basics of Python: Operations, loops, functions, dictionaries
- Numpy – creating arrays, reading, writing, manipulation techniques
- Ground-up for Deep-Learning
Exploratory Data Analysis with Python
- Getting to understand structure of Matplotlib
- Configuring grid, ticks.
- text, color map, markers, widths with Matplotlib
- configuring axes, grid,
- hist, scatterplots
- bar charts
- multiple plots
- 3D plots
Data Munging with Python
- Introduction to pandas
- Data loading with Pandas
- Data types with python
- Descriptive Statistics with Pandas
- Quartile analysis with Pandas
- Sort, Merge, join with Pandas
- Indexing and Slicing with pandas
Introduction to Artificial Intelligence
- Dealing Prediction problem
- Forecasting for industry
- Optimization in logistics
- Segmentation in customer analytics
- Supervised learning
Artificial Intelligence I - Statistical Modelling
- Linear Regression
- Assumptions
- Model development and interpretation
- Sum of least squares
Artificial Intelligence II – Machine Learning
- Supervised Learning
- Decision trees and Random Forest
- Classification and Regression trees(CART)
- Process of tree building
- Entropy and Gini Index
- Problem of over fitting
- Pruning a tree back
- Trees for Prediction (Linear) – example
Artificial Intelligence III – Natural Language Processing
- NLP I - Text Preprocessing
- Tokenization
- Stemming
- Lemmatization
- NLP II – Text Modelling
Artificial Intelligence IV - Deep Learning
- ReLU
- Sigmoid, Depth vs Width tradeoffs
- Convolutional networks
- Concepts of filters
- Sliding
Practical use cases of AI and best practices in AI
- Business problem to an analytical problem
- Guidelines in model development
Big Data, Azure for AI, Data Science applications
- Big data and analytics?
- Leverage Big data platforms for Data Science
- Introduction to evolving tools
- Machine learning with Spark
Analytical Visualisation with Tableau
- Why is it important for Data-Analyst
- Tableau workbook walkthrough
- Instruction of creation of your own workbooks
- Demo of few more workbooks
