Data Analyst Masterclass- Complete Data Analytics in Python

Data Analyst Masterclass: Complete Data Analytics in Python, available at $54.99, has an average rating of 4.78, with 127 lectures, 94 quizzes, based on 33 reviews, and has 1118 subscribers.

You will learn about You will get proficient in Python for thorough data analysis. Prepare for a career as a data analyst by acquiring?practical?skills and expertise. You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning. You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc. You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python. You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models. You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises. You will pass practical assignments, 85+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire course. You will accomplish one capstone project on Sport data analysis at the end to get the full view of data analysis workflow in Python. This course is ideal for individuals who are Those who are highly interested in learning complete data analytics using Python. or This course is NOT for those who are interested to learn data science or advanced machine learning application. It is particularly useful for Those who are highly interested in learning complete data analytics using Python. or This course is NOT for those who are interested to learn data science or advanced machine learning application.

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Summary

Title: Data Analyst Masterclass: Complete Data Analytics in Python

Price: $54.99

Average Rating: 4.78

Number of Lectures: 127

Number of Quizzes: 94

Number of Published Lectures: 127

Number of Published Quizzes: 94

Number of Curriculum Items: 227

Number of Published Curriculum Objects: 227

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

You will get proficient in Python for thorough data analysis. Prepare for a career as a data analyst by acquiring?practical?skills and expertise.

You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.

You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.

You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.

You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.

You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.

You will pass practical assignments, 85+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire course.

You will accomplish one capstone project on Sport data analysis at the end to get the full view of data analysis workflow in Python.

Who Should Attend

Those who are highly interested in learning complete data analytics using Python.

This course is NOT for those who are interested to learn data science or advanced machine learning application.

Target Audiences

Those who are highly interested in learning complete data analytics using Python.

This course is NOT for those who are interested to learn data science or advanced machine learning application.

Welcome to the Data Analyst Masterclass: Complete Data Analysis in Python! In this comprehensive course, you’ll embark on a journey from Python novice to proficient data analyst, equipped with the essential skills and knowledge to excel in the field.

Throughout this course, you will delve deep into the realm of Python programming, focusing on its application in data analysis. Starting from the basics, you’ll master fundamental concepts such as variable naming, data types, lists, dictionaries, dataframes, sets, loops, and functions. With a solid foundation in Python, you’ll seamlessly transition to advanced topics, including data cleaning, sorting, filtering, manipulation, transformation, and preprocessing.

But that’s not all. As you progress, you’ll learn how to harness the power of Python for data visualization, exploratory data analysis, statistical analysis, hypothesis testing, and even delve into the exciting world of machine learning. Through a combination of theoretical understanding and hands-on practice, you’ll gain proficiency in a wide range of methods and techniques essential for data analysis.

What sets this course apart is its emphasis on practical application. You won’t just learn the theory; you’ll put your newfound knowledge to the test through practical data analysis projects and hands-on exercises. With over 85 coding exercises, 10 quizzes featuring 100+ questions, and practical assignments covering all topics, you’ll have ample opportunities to reinforce your skills and enhance your problem-solving abilities.

As the culmination of your journey, you’ll undertake a capstone project focused on sports data analysis. This final project will allow you to apply all the skills you’ve acquired throughout the course, providing you with a comprehensive understanding of the data analysis workflow in Python.

Whether you’re a seasoned professional looking to upskill or someone just starting their journey in data analysis, this course is designed to equip you with the expertise and confidence needed to succeed. Join us on this exciting adventure and unlock your potential as a data analyst in Python.

Course Curriculum

Chapter 1: Start Here: MUST Follow the Instructions

Lecture 1: Instructions to accomplish the course

Lecture 2: Python cheatsheet for data analysis

Lecture 3: Connect with my youtube channel

Lecture 4: Get my special handbooks

Lecture 5: Resources used in the course

Chapter 2: Data Analysis and Its Application

Lecture 1: Understanding analyzing data

Lecture 2: Real-world application of data analysis

Chapter 3: Data Analysis Tools, Techniques and Methods

Lecture 1: Various aspects of data cleaning

Lecture 2: Various aspects of Joining datasets

Lecture 3: Methods of exploratory data analysis Part 1

Lecture 4: Methods of exploratory data analysis Part 2

Lecture 5: Methods of exploratory data analysis Part 3

Chapter 4: Statistical Analysis Methods and Techniques

Lecture 1: Population v/s sample and its methods

Lecture 2: Types of statistical data analysis

Lecture 3: A Recap on descriptive statistics methods

Lecture 4: Inferential statistics Part 1 – T-tests and ANOVA

Lecture 5: Inferential statistics Part 2 – Relationships measures

Lecture 6: Inferential statistics Part 3 – Linear regression

Chapter 5: Clarifying the Concept of Hypothesis Testing

Lecture 1: Hypothesis testing for inferential statistics

Lecture 2: Selecting statistical test and assumption testing

Lecture 3: Confidence level, significance level, p-value

Lecture 4: Making decision and conclusion on findings

Lecture 5: A-Z statistical analysis and hypothesis testing

Chapter 6: Data Transformation and Visualisation Methods

Lecture 1: Techniques for data transformation Part 1

Lecture 2: Techniques for data transformation Part 2

Lecture 3: Several methods of data visualization Part 1

Lecture 4: Several methods of data visualization Part 2

Lecture 5: Several methods of data visualization Part 3

Chapter 7: Data Modeling with Machine Learning Model

Lecture 1: Importance of ML in data analytics

Lecture 2: Widely used machine learning models

Lecture 3: Steps in developing machine learning model

Chapter 8: Setting Up Python and Jupyter Notebook

Lecture 1: Installing Python and Jupyter Notebook – Mac

Lecture 2: Installing Python and Jupyter Notebook – Windows

Lecture 3: More alternative methods – Check the article

Chapter 9: Starting with Variables to Data Types

Lecture 1: Getting started with first python code

Lecture 2: Assigning variable names correctly

Lecture 3: Various data types and data structures

Lecture 4: Converting and casting data types

Lecture 5: Starting with Variables to Data Types

Chapter 10: Various Operators in Python Programming

Lecture 1: Arithmetic operators (+, -, *, /, %, **)

Lecture 2: Comparison operators (>, <, >=, <=, ==, !=)

Lecture 3: Logical operators (and, or, not)

Lecture 4: Operators in Python Programming

Chapter 11: Dealing with Data Structures

Lecture 1: Lists: creation, indexing, slicing, modifying

Lecture 2: Sets: unique elements, operations

Lecture 3: Dictionaries: key-value pairs, methods

Lecture 4: Several data structures

Chapter 12: Conditionals Looping and Functions

Lecture 1: Conditional statements (if, elif, else)

Lecture 2: Nested logical expressions in conditions

Lecture 3: Looping structures (for loops, while loops)

Lecture 4: Defining, creating, and calling functions

Lecture 5: Conditions loops and functions

Chapter 13: Sequential Cleaning and Modifying Data

Lecture 1: Preparing notebook and loading data

Lecture 2: Identifying missing or null values

Lecture 3: Method of missing value imputation

Lecture 4: Exploring data types in a dataframe

Instructors

Shahriars Analytical Academy
Empowering Enthusiasts in the World of Data Analytics

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3 stars: 2 votes

4 stars: 2 votes

5 stars: 29 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!