Data Science with Python

Uplatz offers this comprehensive course on Data Science with Python. It is a self-paced video course. You will be awarded Course Completion Certificate at the end of the course.

Data science is an interdisciplinary subject that use scientific techniques, procedures, algorithms, and systems to extract information and insights from structured and unstructured data, as well as to apply that knowledge and actionable insights to a variety of application areas. Data mining, machine learning, and big data are all linked to data science. Data science is an interdisciplinary approach to deriving meaningful insights from today's businesses' massive and ever-increasing amounts of data. Preparing data for analysis and processing, doing sophisticated data analysis, and presenting the results to expose trends and allow stakeholders to make educated decisions are all part of data science.

Data science is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components i.e. Data exploration & analysis : Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library and Data visualization - Taking data and turning it into something colorful : Matplotlib; Seaborn; Datashader.

This Data Science course will teach you the fundamentals of data science. This course is particularly beneficial to students who have no prior experience with data science. This course will teach you how to prepare data by cleaning, aggregating, and modifying it so that it is ready for particular sorts of processing.

In this Data Science with Python course by Uplatz you will learn to:

• Understand Python language basics and how they apply to data science.
• Practice iterative data science using Jupyter notebooks on IBM Cloud.
• Analyze data using Python libraries like pandas and numpy.
• Create stunning data visualizations with matplotlib, folium, and seaborn.
• Build machine learning models using scipy and scikitlearn.
• Demonstrate proficiency in solving real life data science problems.

Uplatz’s Data Science with Python course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. Upon course completion, you will get a certificate issued by Uplatz.

Course Outcomes

• Explain how these results can be used to solve business problems.
• Use mathematics, statistics, and the scientific method to solve problems
• Evaluate and prepare data using a variety of tools and techniques, including SQL, data mining, and data integration approaches.
• Use predictive analytics, such as machine learning and deep learning models, to extract insights from data.
• Create data-processing and calculation-automation apps.

Certificate of completion

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included

• Data Science with Python - all lectures 44:34:25

  • 1: Introduction to Data Science Preview 1:01:15
  • 2: Introduction to Python Programming 59:20
  • 3: Part 1 - Variables and Data Types 27:05
  • 4: Part 2 - Variables and Data Types 55:28
  • 5: Part 1 - Input-Output - Keywords - Identifiers 49:19
  • 6: Part 2 - Input-Output - Keywords - Identifiers 44:10
  • 7: Part 1 - Operators and Types of Operators 27:52
  • 8: Part 2 - Operators and Types of Operators 31:22
  • 9: Decision Making 45:24
  • 10: Part 1 - Loops in Python 32:47
  • 11: Part 2 - Loops in Python 39:43
  • 12: Part 3 - Loops in Python 23:14
  • 13: Part 1 - List in Python 46:54
  • 14: Part 2 - List in Python 40:31
  • 15: Part 1 - Tuples and Dictionary 53:33
  • 16: Part 2 - Tuples and Dictionary 51:22
  • 17: Part 1 - Functions and Modules 44:02
  • 18: Part 2 - Functions and Modules 43:16
  • 19: Part 3 - Functions and Modules 48:22
  • 20: Part 1 - Working with Files 1:05:10
  • 21: Part 2 - Working with Files 26:50
  • 22: Regular Expression 1:02:45
  • 23: Introduction to Data Science Libraries 45:36
  • 24: Components of Python Ecosystem 54:24
  • 25: Part 1 - Analysing Data using NumPy and Pandas 56:58
  • 26: Part 2 - Analysing Data using NumPy and Pandas 43:37
  • 27: Part 3 - Analysing Data using NumPy and Pandas 37:22
  • 28: Part 4 - Analysing Data using NumPy and Pandas 1:12:54
  • 29: Part 5 - Analysing Data using NumPy and Pandas 28:31
  • 30: Part 1 - Data Visualisation with Matplotlib 37:46
  • 31: Part 2 - Data Visualisation with Matplotlib 43:41
  • 32: Part 3 - Data Visualisation with Matplotlib 1:09:26
  • 33: Three-Dimensional Plotting with Matplotlib 1:03:43
  • 34: Part 1 - Data Visualisation with Seaborn 41:53
  • 35: Part 2 - Data Visualisation with Seaborn 59:16
  • 36: Introduction to Statistical Analysis 1:15:05
  • 37: Part 1 - Data Science Methodology - Basics 47:34
  • 38: Part 2 - Data Science Methodology - Basics 44:12
  • 39: Data Science Methodology - Advanced 39:25
  • 40: Introduction to Machine Learning and its Types 56:18
  • 41: Part 1 - Regression Analysis 1:11:51
  • 42: Part 2 - Regression Analysis 54:25
  • 43: Part 3 - Regression Analysis 38:19
  • 44: Part 1 - Classification 1:08:44
  • 45: Part 2 - Classification 38:37
  • 46: Part 3 - Classification 25:37
  • 47: Part 1 - Clustering 53:11
  • 48: Part 2 - Clustering 28:56
  • 49: Part 3 - Clustering 33:59
  • 50: Part 1 - Association Rule Learning 53:30
  • 51: Part 2 - Association Rule Learning 58:45
  • 52: Part 1 - Project - Loan Prediction by applying Data Science 56:41
  • 53: Part 2 - Project - Loan Prediction by applying Data Science 42:39
  • 54: Part 3 - Project - Loan Prediction by applying Data Science 50:55
  • 55: Part 4 - Project - Loan Prediction by applying Data Science 38:19
  • 56: Part 5 - Project - Loan Prediction by applying Data Science 1:02:32

Data Science with Python – Course Syllabus

1. Introduction to Data Science
• Introduction to Data Science
• Python in Data Science
• Why is Data Science so Important?
• Application of Data Science
• What will you learn in this course?

2. Introduction to Python Programming
• What is Python Programming?
• History of Python Programming
• Features of Python Programming
• Application of Python Programming
• Setup of Python Programming
• Getting started with the first Python program

3. Variables and Data Types
• What is a variable?
• Declaration of variable
• Variable assignment
• Data types in Python
• Checking Data type
• Data types Conversion
• Python programs for Variables and Data types

4. Python Identifiers, Keywords, Reading Input, Output Formatting
• What is an Identifier?
• Keywords
• Reading Input
• Taking multiple inputs from user
• Output Formatting
• Python end parameter

5. Operators in Python
• Operators and types of operators
- Arithmetic Operators
- Relational Operators
- Assignment Operators
- Logical Operators
- Membership Operators
- Identity Operators
- Bitwise Operators
• Python programs for all types of operators

6. Decision Making
• Introduction to Decision making
• Types of decision making statements
• Introduction, syntax, flowchart and programs for
- if statement
- if…else statement
- nested if
• elif statement

7. Loops
• Introduction to Loops
• Types of loops
- for loop
- while loop
- nested loop
• Loop Control Statements
• Break, continue and pass statement
• Python programs for all types of loops

8. Lists
• Python Lists
• Accessing Values in Lists
• Updating Lists
• Deleting List Elements
• Basic List Operations
• Built-in List Functions and Methods for list

9. Tuples and Dictionary
• Python Tuple
• Accessing, Deleting Tuple Elements
• Basic Tuples Operations
• Built-in Tuple Functions & methods
• Difference between List and Tuple
• Python Dictionary
• Accessing, Updating, Deleting Dictionary Elements
• Built-in Functions and Methods for Dictionary

10. Functions and Modules
• What is a Function?
• Defining a Function and Calling a Function
• Ways to write a function
• Types of functions
• Anonymous Functions
• Recursive function
• What is a module?
• Creating a module
• import Statement
• Locating modules

11. Working with Files
• Opening and Closing Files
• The open Function
• The file Object Attributes
• The close() Method
• Reading and Writing Files
• More Operations on Files

12. Regular Expression
• What is a Regular Expression?
• Metacharacters
• match() function
• search() function
• re.match() vs re.search()
• findall() function
• split() function
• sub() function

13. Introduction to Python Data Science Libraries
• Data Science Libraries
• Libraries for Data Processing and Modeling
- Pandas
- Numpy
- SciPy
- Scikit-learn
• Libraries for Data Visualization
- Matplotlib
- Seaborn
- Plotly

14. Components of Python Ecosystem
• Components of Python Ecosystem
• Using Pre-packaged Python Distribution: Anaconda
• Jupyter Notebook

15. Analysing Data using Numpy and Pandas
• Analysing Data using Numpy & Pandas
• What is numpy? Why use numpy?
• Installation of numpy
• Examples of numpy
• What is ‘pandas’?
• Key features of pandas
• Python Pandas - Environment Setup
• Pandas – Data Structure with example
• Data Analysis using Pandas

16. Data Visualisation with Matplotlib
• Data Visualisation with Matplotlib
- What is Data Visualisation?
- Introduction to Matplotlib
- Installation of Matplotlib
• Types of data visualization charts/plots
- Line chart, Scatter plot
- Bar chart, Histogram
- Area Plot, Pie chart
- Boxplot, Contour plot

17. Three-Dimensional Plotting with Matplotlib
• Three-Dimensional Plotting with Matplotlib
- 3D Line Plot
- 3D Scatter Plot
- 3D Contour Plot
- 3D Surface Plot

18. Data Visualisation with Seaborn
• Introduction to seaborn
• Seaborn Functionalities
• Installing seaborn
• Different categories of plot in Seaborn
• Exploring Seaborn Plots

19. Introduction to Statistical Analysis
• What is Statistical Analysis?
• Introduction to Math and Statistics for Data Science
• Terminologies in Statistics – Statistics for Data Science
• Categories in Statistics
• Correlation
• Mean, Median, and Mode
• Quartile

20. Data Science Methodology (Part-1)
Module 1: From Problem to Approach
• Business Understanding
• Analytic Approach
Module 2: From Requirements to Collection
• Data Requirements
• Data Collection
Module 3: From Understanding to Preparation
• Data Understanding
• Data Preparation

21. Data Science Methodology (Part-2)
Module 4: From Modeling to Evaluation
• Modeling
• Evaluation
Module 5: From Deployment to Feedback
• Deployment
• Feedback
Summary

22. Introduction to Machine Learning and its Types
• What is a Machine Learning?
• Need for Machine Learning
• Application of Machine Learning
• Types of Machine Learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning

23. Regression Analysis
• Regression Analysis
• Linear Regression
• Implementing Linear Regression
• Multiple Linear Regression
• Implementing Multiple Linear Regression
• Polynomial Regression
• Implementing Polynomial Regression

24. Classification
• What is Classification?
• Classification algorithms
• Logistic Regression
• Implementing Logistic Regression
• Decision Tree
• Implementing Decision Tree
• Support Vector Machine (SVM)
• Implementing SVM

Everyone

Passion to achieve your goals!

• Data Scientist - AI/ML Python
• Principal Data Scientist
• Data Science Engineer
• Machine Learning Engineer
• Data Engineer
• Python Developer
• Data Science Manager
• Data Science Analyst — Smart Devices
• Product Data Scientist
• Data Analyst
• Data Visualization Developer
• Data Science Consultant
• Supply Chain Specialist - Data Science
• Lead Data Scientist
• Software Engineer - Data Science
• Chief Data Scientist