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Development Data Science

Data Science with Python Certification Training and Project

Start your career as Data Scientist from scratch. Learn Data Science with Python. Predict trends with advanced analytics
Rating: 4.1 out of 54.1 (183 ratings)
30,428 students
Created by Uplatz Training
Last updated 12/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • End-to-end knowledge of Data Science
  • Prepare for a career path as Data Scientist / Consultant
  • Overview of Python programming and its application in Data Science
  • Detailed level programming in Python - Loops, Tuples, Dictionary, List, Functions & Modules, etc.
  • Decision-making and Regular Expressions
  • Introduction to Data Science Libraries
  • Components of Python Ecosystem
  • Analysing Data using Numpy and Pandas
  • Data Visualisation with Matplotlib
  • Three-Dimensional Plotting with Matplotlib
  • Data Visualisation with Seaborn
  • Introduction to Statistical Analysis - Math and Statistics
  • Terminologies & Categories of Statistics, Correlation, Mean, Median, Mode, Quartile
  • Data Science Methodology - From Problem to Approach, From Requirements to Collection, From Understanding to Preparation
  • Data Science Methodology - From Modeling to Evaluation, From Deployment to Feedback
  • Introduction to Machine Learning
  • Types of Machine Learning - Supervised, Unsupervised, Reinforcement
  • Regression Analysis - Linear Regression, Multiple Linear Regression, Polynomial Regression
  • Implementing Linear Regression, Multiple Linear Regression, Polynomial Regression
  • Classification, Classification algorithms, Logistic Regression
  • Decision Tree, Implementing Decision Tree, Support Vector Machine (SVM), Implementing SVM
  • Clustering, Clustering Algorithms, K-Means Clustering, Hierarchical Clustering
  • Agglomerative & Divisive Hierarchical clustering
  • Implementation of Agglomerative Hierarchical Clustering
  • Association Rule Learning
  • Apriori algorithm - working and implementation

Course content

26 sections • 56 lectures • 44h 33m total length

  • Preview01:01:14

  • Introduction to Python Programming
    59:19

  • Variables and Data Types - part 1
    27:05
  • Variables and Data Types - part 2
    55:27

  • Input-Output, Keywords, Identifiers - part 1
    49:19
  • Input-Output, Keywords, Identifiers - part 2
    44:09

  • Operators and Types of Operators - part 1
    27:52
  • Operators and Types of Operators - part 2
    31:22

  • Decision-Making
    45:23

  • Loops in Python - part 1
    32:47
  • Loops in Python - part 2
    39:43
  • Loops in Python - part 3
    23:13

  • List in Python - part 1
    46:54
  • List in Python - part 2
    40:30

  • Tuples in Dictionary - part 1
    53:32
  • Tuples in Dictionary - part 2
    51:22

  • Functions and Modules - part 1
    44:01
  • Functions and Modules - part 2
    43:16
  • Functions and Modules - part 3
    48:21

Requirements

  • Enthusiasm and determination to make your mark on the world!

Description

Data Science with Python Programming - 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


25. Clustering

  • What is Clustering?

  • Clustering Algorithms

  • K-Means Clustering

  • How does K-Means Clustering work?

  • Implementing K-Means Clustering

  • Hierarchical Clustering

  • Agglomerative Hierarchical clustering

  • How does Agglomerative Hierarchical clustering Work?

  • Divisive Hierarchical Clustering

  • Implementation of Agglomerative Hierarchical Clustering


26. Association Rule Learning

  • Association Rule Learning

  • Apriori algorithm

  • Working of Apriori algorithm

  • Implementation of Apriori algorithm

Who this course is for:

  • Data Scientists
  • Data Analysts / Data Consultants
  • Senior Data Scientists / Data Analytics Consultants
  • Newbies and beginners aspiring for a career in Data Science
  • Data Engineers
  • Machine Learning Engineers
  • Software Engineers and Programmers
  • Python Developers
  • Data Science Managers
  • Machine Learning / Data Science SMEs
  • Digital Data Analysts
  • Anyone interested in Data Science, Data Analytics, Data Engineering

Instructor

Uplatz Training
Fastest growing Global IT Training Provider
Uplatz Training
  • 3.9 Instructor Rating
  • 5,457 Reviews
  • 280,681 Students
  • 85 Courses

Uplatz is UK-based leading IT Training provider serving students across the globe. Our uniqueness comes from the fact that we provide online training courses at a fraction of the average cost of these courses in the market.

Over a short span of 3 years, Uplatz has grown massively to become a truly global IT training provider with a wide range of career-oriented courses on cutting-edge technologies and software programming.

Our specialization includes Data Science, Data Engineering, SAP, Oracle, Salesforce, AWS, Microsoft Azure, Google Cloud, IBM Cloud, SAS, Python, R, JavaScript, Java, Full Stack Web Development, Mobile App Development, BI & Visualization, Tableau, Power BI, Spotfire, Data warehousing, ETL tools, Informatica, IBM Data Stage, Digital Marketing, Agile, DevOps, and more.

Founded in March 2017, Uplatz has seen phenomenal rise in the training industry starting with an online course on SAP FICO and now providing training on 5000+ courses across 103 countries having served 300,000 students in a period of just 3 years.

Uplatz's training courses are highly structured, subject-focused, and job-oriented with strong emphasis on practice and assignments. Our courses are designed and taught by more than a thousand highly skilled and experienced tutors who have strong expertise in their areas whether it be AWS, Azure, Adobe, SAP, Oracle, or any other technology or in-demand software.

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