Data science combines mathematics, statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning to extract knowledge and insights from structured and unstructured data and apply this knowledge from data across a broad range of application areas in life such as decision making in businesses, strategic planning, etc.
Curriculum
Domain 1—BUSINESS INTELLIGENCE
A. Introduction to Business Intelligence
- what is business intelligence?
- why business intelligence
- Business intelligence tools
- Business intelligence vs business analytics
B. Business intelligence with Microsoft Excel
- Introduction to Excel
- Data cleaning and preparation
- Formatting, conditional formatting
- Sorting and Advanced filtering
- Functions and formulas
- Data validation
- Business Analytics with excel
- Data visualization and dashboarding
- Excel-VB and macro
C. Business intelligence with Tableau
- Introduction
- Connecting to various data sources
- Tableau Advanced reports
- Structuring Data
- Filters
- Charts and graphs
- Data visualization
- Working with Metadata
- Calculations
- Dashboard and stories
Domain 2—STATISTICS AND PROBABILITY
A. Descriptive Statistics
- Describing data with tables and graphs
- Frequency distribution
- Describing data with Averages
- Describing data with Variability
- Normal distributions and Standard (Z) Scores
- Correlation
- Regression
B. Probability and Inferential Statistics
- Population and samples
- Probability
- Sampling distribution of Mean
- Hypothesis Testing-Z Test
- Estimation (Confidence Intervals)
- T-Test for one sample
- T-Test for two samples
- Analysis of variance (One factor)
- Analysis of variance (two factors)
- Chi-Square
Domain 3—PROGRAMMING
A. Python programming
- Python Basics
- Python Data Structure
- Conditional statements
- Logical statements
- Functions
- Working with files
- Modules
- OOP
- Python Libraries for Data Science
- Data wrangling
B. Python libraries
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- Seaborn
- Scrappy
- Beautiful soup
- TensorFlow
- Porch
- Spacy
- OpenCV
- Keras
- NLTK
- Gensim
C. Data Science
- What is data science
- Roles of a Data scientist
- Data science life cycle
- Tools used in Data Science
- Big Data
Domain 4—MACHINE LEARNING
A. Introduction of machine learning
- What is machine learning
- Application of machine learning
- Machine learning life cycle
- AI vs Machine learning
- Machine learning categories
- Data processing in Machine Learning
- Scikit learn
- Scipy
B. Supervised Learning
- What is supervised learning
- Types of supervised learning
- Regression
- Classification
C. Unsupervised Machine Learning
- Advantages and disadvantages
- Association
- Clustering
E. Dimensionality Reduction
- What is Dimensionality
- Curse of Dimensionality
- Advantages and disadvantages of dimensionality
- Approaches of Dimension Reduction
- Techniques of dimensionality reduction
- Principal component Analysis (PCA)
- Factor Analysis
- Scaling dimensional model
- LDA
Domain 5—DEEP LEARNING
- Introduction of deep learning
- What is Deep learning?
- Deep learning frameworks
- Artificial Neural Networks (ANN)
- Backpropagation & Gradient Descent
- Recurrent Neural Network(RNN)
- Convolutional Neural Network (CNN)
- Autoencoders
- Restricted Boltzmann Machine
- TensorFlow Object Detection
- Chatbots creation
B.Natural Language Preprocessing
Domain 5—DATABASE
A. SQL
- What is SQL?
- SQL-RDBMS Concepts(
- Types of RDBMS
- Database Normalization
- SQL Syntax
- Data types
- Operators
- SQL commands
- DDL (create, Alter, Drop)
- DML (Insert, Update, Delete)
- DCL (Grant, Revoke)
- DQL (Select)
- SQL constraint
- SQL clause
- Min, Max, Count, Avg, Sum, Like, Wildcard
- Joins
Domain 5—GIT
A. Introduction to GIT AND GITHUB
- Git lifecycle
- Git commands
- Working with Git
- Github collaboration
- GitHub authentication
- Git branches and Git branch Merge
- Git workflow
Who should learn Data science?
- System Administrator
- Analytics manager
- Tableau Consultant
- Business Consultant
- IT consultant
- Analytics consultant
- Data analyst
- Senior data analyst
- IT Representative
- Business intelligence analyst
- IT Implementer
- Business Intelligence developer
What will I be able to do at the end of the training?
- Design models for businesses to solve real-world problems
- Communicate predictions and findings to management and IT departments via data visualization and report.
- Ability to use SQL and other visualization tools for effective data visualization.
