Data Analytics & AI, Machine Learning

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This professional technical course designed to teach Data Analytics & AI, Machine Learning in a hands-on manner and prepare the participants for a career in this field. The professional technical course will provide you with the complete toolbox to become a Python Fundamentals Data Scientist. At completion, students will have gained the analytical skills required to open the doors to a lucrative career as a Python and Data Scientist. Data is extremely important to all organizations, and at all levels. It’s not just big IT and software companies. LinkedIn recently picked Data Analytics & AI, Machine Learning engineer as its most promising career of 2023. One of the reasons it got the top spot was that the average salary for people in the role is $130,000.

 

  1. industry-Based Course Curriculum
  2. Value Adds: Python Programming, Fundamentals of R, Business Statistics, SAS and ChatGPT
  3. Work Hands-on With 20+ Labs, 30+ Assignments, and 100+ Interview Preparation Questions
  4. Support through WhatsApp, Calls, & Emails

 

1. Python for Data Science
2. Database Management Supervisor
3. Technical Architect
4. Data Processing Specialist
5. Power BI
6. Students will learn about functions and loops in Python.
7. Hands-on experience with Jupyter Hub and Python libraries such as Pandas, NumPy, Scipy etc.
8. What is data analytics?
9. What’s the difference between data analytics and data science?
10. What are the different types of data analysis?
11. What are some real-world data analytics examples?
12. What does a data analyst do?
13. What is the typical process that a data analyst will follow?
14. What tools and techniques do data analysts use?
15. Data analytics techniques
16. Data analytics tools
17. What skills do you need to become a data analyst?
18. What does the future hold for data analytics?
19. Key takeaways and further reading
20. They will also learn how create amazing visuals in Python Using Matplotlib, Bokeh and
21. Download and Install Python / Text Editor
22. Syntax & Commenting
23. Variables & Data Types
24. Composite Data Types
25. Numbers
26. Lists
27. Strings
28. Tuples
29. Sets & Frozensets
30. Dictionaries
31. If Loops
32. While Loops
33. For Loops
34. Functions
35. Class Objects
36. Numbers & Strings in Python
37. Working with Lists in Python
38. NumPy in Python
39. Data Visualization in Python (Matplotlib & Seaborn)
40. Pandas Library in Python
41. Manipulating Data Frames
42. Conditionals & Loops in Python
43. Project

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What's included

  • 1. Python for Data Science
  • 2. Database Management Supervisor
  • 3. Technical Architect
  • 4. Data Processing Specialist
  • 5. Power BI
  • 6. Students will learn about functions and loops in Python.
  • 7. Hands-on experience with Jupyter Hub and Python libraries such as Pandas, NumPy, Scipy etc.
  • 8. What is data analytics?
  • 9. What’s the difference between data analytics and data science?
  • 10. What are the different types of data analysis?
  • 11. What are some real-world data analytics examples?
  • 12. What does a data analyst do?
  • 13. What is the typical process that a data analyst will follow?
  • 14. What tools and techniques do data analysts use?
  • 15. Data analytics techniques
  • 16. Data analytics tools
  • 17. What skills do you need to become a data analyst?
  • 18. What does the future hold for data analytics?
  • 19. Key takeaways and further reading
  • 20. They will also learn how create amazing visuals in Python Using Matplotlib, Bokeh and
  • 21. Download and Install Python / Text Editor
  • 22. Syntax & Commenting
  • 23. Variables & Data Types
  • 24. Composite Data Types
  • 25. Numbers
  • 26. Lists
  • 27. Strings
  • 28. Tuples
  • 29. Sets & Frozensets
  • 30. Dictionaries
  • 31. If Loops
  • 32. While Loops
  • 33. For Loops
  • 34. Functions
  • 35. Class Objects
  • 36. Numbers & Strings in Python
  • 37. Working with Lists in Python
  • 38. NumPy in Python
  • 39. Data Visualization in Python (Matplotlib & Seaborn)
  • 40. Pandas Library in Python
  • 41. Manipulating Data Frames
  • 42. Conditionals & Loops in Python
  • 43. Project

What Will I Learn?

  • The professional technical course program will provide instruction and hands-on training for the participants to feel confident to start working in the Data Analytics & AI, Machine Learning industry. At the end of this program participants will learn to understand the how to manage Python projects throughout the life cycle. The professional technical course will cover essential exploratory techniques for summarizing data. Students will learn how to prep their data for training their machine learning models. With the tools and skills taught in this section of the course, students will be prepared to work with real data, make discoveries, and present compelling results using Python. This course will cover basic components of building and applying prediction functions with an emphasis on practical applications. Students will learn all state-of-the-art AI & Machine learning models including how to create, test, train and deploy these models. Students will learn several techniques, including supervised learning and theoretical aspects of machine learning.

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