Basic Database, Data Analytics, Data Science and API Management

Basic Database, Data Analytics, Data Science and API Management

(4 days)

This course teaches you core data science and data analytics concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, machine learning, visualization and Python programming, helping you build a solid foundation to gain proficiency in data science.

The course starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples.

As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn.

You’ll also be able to call APIs to connect with Relational Database Management Systems.

By the end of the course, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.

Learning Outcome

By the end of this course, the learner shall have the following skills:

• Understand what happens over the course of a system's life (SDLC)
• Establish what to expect from the pre-development life cycle steps.
• Find out how the development-specific phases of the SDLC affect development.
• Identify the existence of project-independent best practices and how to use them.
• Find out how to design and implement a high-performance computing process.
• Basic python programming
• Understand the basic syntax structures.
• Get updated on recent syntax changes in python 3.8 and 3.9 respectively.
• Advanced Data handling in python.
• Working with external libraries in python.
• Working with files and encoding.
• Understand different forms of data acquisition.
• Data Extraction and conversion.
• Learn statistical analytics of data.
• Use Python to get the basics of Data Analytics.
• Use simple scripting to present data.
• Have the ability to visualize data and manipulate them using simple python.
• Understand how data science delves into Machine Learning.
• Machine learning and coding in python using Pandas, SciPy,Scikit Learn,Matplotlib, Seaborn.
• Machine learning and coding in python usingXGBoost
• Using machine learning for predictions and analytics.
• Data Acquisition and scraping.
• Understanding of how Machine Learning works
• Linux basics
• RDBMS
• Remote queries
• SSH

Prerequisites

• High speed internet connection
• Ability to use a computer
• Ability to use the internet
• Web camera (for remote learning only)
• Microphone (for remote learning only)
• Conferencing software (Zoom / Webex / MS Teams)
• Manage files and understand basic file systems in the computer
• Ability to communicate in the English language
• Ability to connect to external servers
• Full admin / root access

Course Outline

1. Machine Learning
   1. Advanced Visualization with Seaborn.
   2. Covariance and Correlation.
   3. Conditional Probability.
   4. Bayes' Theorem.
   5. Basic Machine Learning (using Python).
   6. Supervised vs. Unsupervised Learning, and Train/Test.
   7. Using Train/Test to Prevent Overfitting a Polynomial Regression.
   8. Bayesian Methods: Concepts.
   9. Practice with:
   10. Implementing a Spam Classifier with Naive Bayes.
   11. K-Means Clustering.
   12. Entropy
   13. Decision Trees
   14. Bias/Variance Tradeoff
   15. K-Fold Cross-Validation to avoid overfitting
   16. Data Cleaning and Normalization
   17. Cleaning web log data
   18. Normalizing numerical data
   19. Detecting outliers
   20. Feature Engineering
   21. Imputation Techniques for Missing Data
   22. Handling Unbalanced Data: Oversampling
2. Database basics
   1. Understanding RDBMS
   2. Understanding Microsoft’s SQl Server
   3. SQL vs T-SQL
   4. Understanding Production level setup
   5. Containers
   6. Remote Server for MS SQL
   7. Containers vs Virtual Box
   8. Installing Containers on Ubuntu
   9. Installing MS SQL in Ubuntu 18.0.4 in a container
   10. Accessing the MS SQL Server
   11. Microsoft SQL Server Management Studio
   12. DBs
   13. Tables
   14. Stored Procedures
   15. Functions
   16. Security
   17. Importing Data
   18. Exporting Data
   19. Security
   20. Azure Data Studio
   21. DBs
   22. Tables
   23. Stored Procedures
   24. Functions
   25. SQL
   26. Select
   27. Where
   28. Like
   29. Order
   30. Insert
   31. Update
   32. Delete
   33. IN Operator
   34. Between
   35. Aggregate
   36. Group
   37. Alter
   38. Sub queries
   39. Stored Procedures
   40. Functions
3. Assessment