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Data Analysis 360: Become Data Analyst in 40 Days Challenge

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Data Analysis 360: Become Data Analyst in 40 Days Challenge, available at $44.99, has an average rating of 4.53, with 179 lectures, 111 quizzes, based on 44 reviews, and has 1182 subscribers.

You will learn about You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning. You will learn how to apply conditional formatting in Excel to visually highlight key trends, insights, and anomalies within your data. You will learn essential Excel formulas and functions such as SUM, AVERAGE, COUNT, IF statements and MORE, enabling you to manipulate data effectively. You will learn to utilize Excel's lookup functions (VLOOKUP, HLOOKUP, XLOOKUP) to efficiently search for and retrieve specific information within datasets. You will learn various graph and chart types in Excel for data visualization, including bar charts, pie charts, scatter plots, and more to communicate insights. You will learn advanced analysis using PivotTables and PivotCharts, enabling you to analyze, and visualize complex datasets with ease and interactivity. You will learn to use Excel's built-in data analysis tools for statistical analysis, i.e., descriptive statistics, t-tests, ANOVA, correlation, and regression. You will learn to design and create dynamic DASHBOARD in Excel, by a visually interactive format for effective decision-making and reporting. 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. This course is ideal for individuals who are Those who are highly interested in learning complete data analytics using Python. or Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel. 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 Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel. or This course is NOT for those who are interested to learn data science or advanced machine learning application.

Enroll now: Data Analysis 360: Become Data Analyst in 40 Days Challenge

Summary

Title: Data Analysis 360: Become Data Analyst in 40 Days Challenge

Price: $44.99

Average Rating: 4.53

Number of Lectures: 179

Number of Quizzes: 111

Number of Published Lectures: 179

Number of Published Quizzes: 111

Number of Curriculum Items: 330

Number of Published Curriculum Objects: 330

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

  • You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.
  • You will learn how to apply conditional formatting in Excel to visually highlight key trends, insights, and anomalies within your data.
  • You will learn essential Excel formulas and functions such as SUM, AVERAGE, COUNT, IF statements and MORE, enabling you to manipulate data effectively.
  • You will learn to utilize Excel's lookup functions (VLOOKUP, HLOOKUP, XLOOKUP) to efficiently search for and retrieve specific information within datasets.
  • You will learn various graph and chart types in Excel for data visualization, including bar charts, pie charts, scatter plots, and more to communicate insights.
  • You will learn advanced analysis using PivotTables and PivotCharts, enabling you to analyze, and visualize complex datasets with ease and interactivity.
  • You will learn to use Excel's built-in data analysis tools for statistical analysis, i.e., descriptive statistics, t-tests, ANOVA, correlation, and regression.
  • You will learn to design and create dynamic DASHBOARD in Excel, by a visually interactive format for effective decision-making and reporting.
  • 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.

Who Should Attend

  • Those who are highly interested in learning complete data analytics using Python.
  • Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel.
  • 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.
  • Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel.
  • This course is NOT for those who are interested to learn data science or advanced machine learning application.

Are you ready to embark on a journey into the world of data analytics? Welcome to Data Analytics 360, where you’ll master two of the most powerful tools in the field: Python and Excel. In this comprehensive course, you’ll dive deep into the foundations of data analysis, from basic statistical concepts to advanced machine learning techniques.

Master the Fundamentals: Gain a solid understanding of data analytics principles, including statistical analysis, hypothesis testing, and machine learning. Whether you’re new to the field or looking to sharpen your skills, this course provides the perfect starting point.

Excel for Data Analysis: Unlock the full potential of Excel as a data analysis tool. Learn essential formulas and functions, harness the power of conditional formatting to identify trends and anomalies, and utilize lookup functions for efficient data retrieval. Discover the art of data visualization with various chart types and master advanced analysis with PivotTables and PivotCharts.

Python Essentials: Dive into Python programming basics, from variables and data types to loops and functions. Explore methods for data cleaning, sorting, filtering, and manipulation, as well as techniques for exploratory data analysis and hypothesis testing. Harness the power of Python libraries for data visualization and machine learning.

Hands-on Projects: Put your skills to the test with practical data analysis projects. From cleaning and preprocessing data to building machine learning models, you’ll tackle real-world challenges and enhance your problem-solving abilities along the way.

Become a Data Analyst: By the end of this course, you’ll have the knowledge and skills to excel as a data analyst. Whether you’re looking to advance your career or explore new opportunities, Data Analytics 360 equips you with the tools you need to succeed in the world of data.

Enroll now and take the first step towards becoming a proficient data analyst with Data Analytics 360.

Course Curriculum

Chapter 1: Day 1 – All You Need to Know about Data Analysis

Lecture 1: Data analysis definition, types and examples

Lecture 2: Key components of data analysis

Lecture 3: Tools and technologies for data analysis

Lecture 4: Real-world application of data analysis

Lecture 5: Connect with my youtube channel

Lecture 6: Get my special handbooks

Chapter 2: Day 2 – Data Collection: Methods and Considerations

Lecture 1: Various sources of collecting data

Lecture 2: Population v/s sample and its methods

Chapter 3: Day 3 – Understand Data Cleaning and Its Methods

Lecture 1: Why you cannot ignore cleaning your data

Lecture 2: Various aspects of data cleaning

Chapter 4: Day 4 – Explore Joining and Concatenating Methods

Lecture 1: Various aspects of Joining datasets

Lecture 2: Adding extra data with concatenation

Chapter 5: Day 5 – Complete Picture of Exploratory Data Analysis

Lecture 1: EDA for generating significant insights

Lecture 2: Methods of exploratory data analysis Part 1

Lecture 3: Methods of exploratory data analysis Part 2

Lecture 4: Methods of exploratory data analysis Part 3

Chapter 6: Day 6 – Everything about Statistical Data Analysis

Lecture 1: The application of statistical test

Lecture 2: Types of statistical data analysis

Lecture 3: Statistical test v/s Exploratory data analysis

Lecture 4: A Recap on descriptive statistics methods

Lecture 5: Inferential statistics Part 1 – T-tests and ANOVA

Lecture 6: Inferential statistics Part 2 – Relationships measures

Lecture 7: Inferential statistics Part 3 – Linear regression

Chapter 7: Day 7 – Concepts of Probabilities in Data Analysis

Lecture 1: Probability in data analysis

Lecture 2: Classical probability

Lecture 3: Empirical probability

Lecture 4: Conditional probability

Lecture 5: Joint probability

Chapter 8: Day 8 – Hypothesis Testing in Statistical Analysis

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: Complete statistical analysis and hypothesis testing

Chapter 9: Day 9 – Explore Data Transformation and Its Methods

Lecture 1: Transforming data for improved analysis

Lecture 2: Techniques for data transformation Part 1

Lecture 3: Techniques for data transformation Part 2

Chapter 10: Day 10 – Machine Learning for Predictive Efficiency

Lecture 1: ML for data analysis and decision-making

Lecture 2: Widely used ML methods in the data analytics

Lecture 3: Steps in developing machine learning model

Chapter 11: Day 11 – Explore Data Visualizations and Its Methods

Lecture 1: Visualizing data for the best insight delivery

Lecture 2: Several methods of data visualization Part 1

Lecture 3: Several methods of data visualization Part 2

Lecture 4: Several methods of data visualization Part 3

Chapter 12: Day 12 – [Excel] Data Cleaning and Formatting

Lecture 1: Identifying and removing duplicates

Lecture 2: Dealing with missing values

Lecture 3: Dealing with outliers

Lecture 4: Finding and imputing inconsistent values

Lecture 5: Text-to-columns for data separation

Chapter 13: Day 13 – [Excel] Data Sorting and Filtering

Lecture 1: Applying sorts & filters to narrow down data

Lecture 2: Advanced filtering with custom criteria

Chapter 14: Day 14 – [Excel] Apply Conditional Formatting

Lecture 1: Highlighting cells based on criteria

Lecture 2: Findings top and bottom insights

Lecture 3: Creating color scales and color bars

Chapter 15: Day 15 – [Excel] Formulas and Functions for Data Analysis

Lecture 1: SUM, AVERAGE, MIN, and MAX functions

Lecture 2: SUMIF, and AVERAGEIF functions

Lecture 3: COUNT, COUNTA, and COUNTIF functions

Lecture 4: YEAR, MONTH and DAY for date manipulation

Lecture 5: IF STATEMENTs for conditional operation

Lecture 6: VLOOKUP for column-wise insight search

Instructors

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  • 4 stars: 14 votes
  • 5 stars: 27 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!

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