Advanced Data Analytics and Data Science
Course Description
This 12-week course provides a comprehensive overview of data analytics, including data collection, visualization, machine learning, predictive analytics, and prescriptive analytics. Students will learn how to use Python for data analysis and apply various techniques to make informed decisions. The course includes hands-on projects and real-world examples to equip students with the skills and knowledge needed to become successful data scientists.
Course Goals
In this Course, you'll learn the following:
- Analytics in Decision Making
- Maturity Model Setup with Tools
- SQL and Python Concepts
- Data Acquisition & Data Profiling
- Statistical Inference and Hypothesis Testing
- Exploratory Data Analysis
- Data Visualization (Python Packages)
- Simple Linear Regression
- Predictive Analytics
- Multiple Linear Regression
- Logistic Regression
- Decision Tree, Support Vector Machine, Neural Network
- AI Tools in Data Science
- Applications of AI in Data Science
Week
1
( 18 Hours )
Introduction and Analytics in Decision Making
- Overview of data analytics
- Importance of analytics in contemporary decision-making processes.
- Exploration of analytics applications in effective decision-making.
- Real-world examples of successful analytics driven decision strategies.
Week
2
( 18 Hours )
Maturity Model Setup, SQL and Python Concepts
- Understanding the maturity model in analytics.
- Practical setup and utilization of analytics tools for maturity assessment.
- Brush up on SQL and Python concepts crucial for data analytics.
- Hands-on exercises to reinforce SQL and Python skills.
Week
3
( 18 Hours )
Data Acquisition & Data Profiling
- Sources and types of data for analytics.
- Techniques for data acquisition, storage, and maintaining data quality.
- Introduction to Azure or Google Cloud Platform for data handling.
Week
4
( 18 Hours )
Statistical Inference - An overview of hypothesis testing
- One sample and two sample t-tests, Z-tests, and chi-square tests.
- Understanding Type I and Type II errors.
- Interpretation of confidence intervals.
Week
5
( 18 Hours )
Exploratory Data Analysis
- Techniques for exploring and summarizing data.
- Introduction to stream analytics pipeline for real-time data examination.
Week
6
( 18 Hours )
Regression and Statistics
- Simple linear regression
- Multiple linear regression
- Non-linear regression
- Logistic regression
- Probability theory
- Descriptive statistics
- Inferential statistics
Week
7
( 18 Hours )
Python for Data Analytics
- Introduction to Python
- Python libraries for data analytics
- Data manipulation and cleaning using Python
- Data visualization using Python
Week
8
( 18 Hours )
Predictive Analytics
- Overview of predictive analytics
- Time series analysis
- ARIMA modeling
- Exponential smoothing
Week
9
( 18 Hours )
Forecasting
- Overview of forecasting
- Time series forecasting
- ARIMA modeling for forecasting
- Exponential smoothing for forecasting
Week
10
( 18 Hours )
Optimizing Decision Making
- Overview of decision making
- Decision trees and random forests for decision making
- Neural networks for decision making
Week
11
( 18 Hours )
Telling Impactful Stories with Data
- Overview of data storytelling
- Data visualization for storytelling
- Communicating insights through storytelling
Week
12
( 18 Hours )
Integrating All Aspects of Data Analytics
- Integrating all aspects of data analytics
- Hands-on project to apply all concepts learned in the course
- Presentation of the project to showcase skills and knowledge acquired in the course
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