Development > Data Science
17 h
£14.99 Free for 2 days
Master Data Analytics & Visualization with Excel & Python
What you will learn:
- Real-world applications of Python in data analysis.
- Setting up Python (Anaconda) on Windows and macOS.
- Python programming fundamentals: variables, data types, operators.
- Essential statistical concepts and their significance.
- Utilizing statistics for impactful data analysis and decision-making.
- Leveraging Python for statistical analysis, including data manipulation and visualization.
- Mastering Excel for data analysis and visualization.
Description
Transform your data skills and unlock career opportunities with our comprehensive data analytics and visualization course. This program provides a practical, hands-on approach to mastering Excel and Python for data analysis. You'll learn to clean, analyze, and visualize data effectively, gaining in-demand technical skills for today's data-driven world.
What you'll achieve:
- Gain practical Python proficiency, utilizing powerful libraries like Pandas and NumPy for data manipulation.
- Unleash the analytical potential of Excel, mastering advanced functions, formulas, and pivot tables.
- Develop a strong foundation in statistical methods, enabling data-driven insights and informed decisions.
- Master data cleaning, preprocessing, and feature engineering techniques for accurate analysis.
- Craft compelling data visualizations using Excel and industry-standard Python libraries like Matplotlib and Seaborn.
- Explore various chart types and best practices for effective data storytelling.
- Understand linear regression and forecasting techniques to predict future trends.
- Dive into core Python programming concepts, including data structures, functions, object-oriented programming, and more.
- Complete hands-on projects to consolidate your learning and build a portfolio.
This course is perfect for aspiring data analysts, business professionals, students, or anyone seeking to enhance their analytical capabilities. Upon completion, you'll possess the practical skills needed to analyze data, extract insights, and present findings effectively in any professional setting. Enroll now and embark on your journey to data mastery!
Curriculum
Fundamentals of Excel
This section covers the basics of Excel, including its applications, interface, sorting, filtering, and conditional formatting. You will also complete a quiz to test your understanding of these fundamental concepts. Lectures include: Excel Applications, Understanding the Excel Interface, Sorting and Filtering, Conditional Formatting, and a Quiz on Excel Fundamentals.
Statistical and Mathematical Functions in Excel
Here, you'll learn about statistical and mathematical functions in Excel. The section includes introductions to statistical and mathematical functions, followed by a quiz to reinforce learning. Lectures include: Introductions to Statistical Functions, Introduction to Mathematical Functions, and a Quiz on Statistical and Mathematical Functions.
Lookup Functions, and Pivot Tables
This section introduces powerful Excel tools: lookup functions and pivot tables. You'll cover lookup functions, index and match, pivot tables, pivot charts, and a final quiz. Lectures include: Introduction to Lookup Functions, Introduction to Index and Match, Introduction to Pivot Tables, Introduction to Pivot Charts, and a Quiz on Lookup Functions, and Pivot Tables.
Logical Functions, and Text Functions
Learn how to use logical and text functions in Excel for data manipulation and formatting. The section includes introductions to logical functions, formatting based on these functions, text functions, formatting based on text functions, and a concluding quiz. Lectures include: Introduction to Logical Function, Formatting Cells based on Logical Functions, Introduction to Text Functions, Formatting cells based on Text Functions, and a Quiz on Logical Functions, and Text Functions.
Data Cleaning, and Feature engineering
This section focuses on data cleaning and feature engineering in Excel, crucial for preparing data for analysis. You'll learn about date and time functions, basics of data cleaning and feature engineering in Excel, Power Query, and a quiz to check your progress. Lectures include: Introduction to Date and Time Functions, Basics of Data Cleaning in Excel, Basics of Feature Engineering in Excel, Introduction to Power Query in Excel, and a Quiz on Data Cleaning and Feature Engineering.
What If analysis
Explore Excel's 'What-If' analysis tools for scenario planning and decision-making. Lectures cover the Scenario Manager, Goal Seek, Data Tables, the Solver Package, and a quiz to assess your understanding. Lectures include: Scenario Manager, Goal Seek, Data Tables, Solver Package, and a Quiz on What If analysis.
Charts and Dashboards
Master the creation of effective charts and dashboards in Excel to visualize data insights. You will learn about data visualization best practices, different chart types, creating and formatting charts, and a quiz for evaluation. Lectures include: Data Visualization Best Practices, Types of Charts in Excel, Creating and Formatting Charts, and a Quiz on Charts and Dashboards.
Linear Regression and Forecasting
This section provides an introduction to linear regression and forecasting techniques. Lectures cover Introduction to Linear Regression and Preliminary Forecasting Analysis.
Basics of Python
Begin your Python journey with this section covering fundamental concepts such as real-world use cases, Anaconda installation, variables, data types, type casting, variable scope, operators and a quiz. Lectures include: Real world use cases of Python, Installation of Anaconda for Windows and macOS, Introduction to Variables, Introduction to Data Types and Type Casting, Scope of Variables, Introduction to Operators, and a Quiz on Basics of Python.
Introduction to Data Structures
Explore various Python data structures, including lists, tuples, sets, dictionaries, stacks, and queues. The section also covers space and time complexity, sorting and searching algorithms, and a quiz. Lectures include: Introduction to Lists and Tuples, Introduction to Sets and Dictionaries, Introduction to Stacks and Queues, Introduction to Space and Time Complexity, Introduction to Sorting Algorithms, Introduction to Searching Algorithms, and a Quiz on Data Structures.
Introduction to Functions in Python
This section teaches you about functions in Python, encompassing parameters, arguments, modules, filter, map, zip, list, set, and dictionary comprehensions, lambda functions, analytical and aggregate functions, and a final quiz. Lectures include: Introduction to Parameters and Arguments, Introduction to Python Modules, Introduction to Filter, Map, and Zip Functions, Introduction to List, Set and Dictionary Comprehensions, Introduction to Lambda Functions, Introduction to Analytical and Aggregate Functions, and a Quiz on Functions in Python.
Strings and Regular Expressions
Master string manipulation and regular expressions in Python. Lectures cover string basics, important string functions, string formatting, user input, metacharacters, built-in regular expression functions, special characters and sets, and a quiz. Lectures include: Introduction to Strings, Introduction to Important String Functions, Introduction to String Formatting and User Input, Introduction to Meta Characters, Introduction to Built-in Functions for Regular Expressions, Special Characters and Sets for Regular Expressions, and a Quiz on Strings and Regular Expressions.
Loops and Conditionals
Learn about control flow in Python with this section covering conditional statements, for loops, while loops, break and continue statements, conditional statements within loops, nested loops and conditionals, and a quiz. Lectures include: Introduction to Conditional Statements, Introduction to For Loops, Introduction to While Loops, Introduction to Break and Continue, Using Conditional Statements in Loops, Nested Loops and Conditional Statements, and a Quiz on Loops and Conditionals.
OOPs and Date-Time
This section introduces object-oriented programming (OOP) concepts and date-time handling in Python. Lectures include introductions to OOP concepts (inheritance, encapsulation, polymorphism), the Date and Time Class, the TimeDelta Class, and a quiz. Lectures include: Introduction to OOPs Concept, Introduction to Inheritance, Introduction to Encapsulation, Introduction to Polymorphism, Introduction to Date and Time Class, Introduction to TimeDelta Class, and a Quiz on OOPs and Date-Time.
Introduction to Statistics
This section introduces the importance of statistics in data analysis. Lectures cover the introduction to statistics and its importance, the role of statistics in data analysis, and Python for statistical analysis with a quiz. Lectures include: Introduction to Statistics and its importance, Explain the role of statistics in data analysis, Introduction to Python for Statistical Analysis, and a Quiz on Introduction to Statistics.
Introduction to Descriptive Statistics
Learn about descriptive statistics, including types of data, measures of central tendency, spread, dependence, shape, position, and standard scores. A quiz follows. Lectures include: Types of Data, Measures of Central Tendency, Measures of Spread, Measures of Dependence, Measures of Shape and Position, Measures of Standard Scores, and a Quiz on Descriptive Statistics.
Introduction to Basic and Conditional Probability
Explore probability concepts, including basic probability, set theory, conditional probability, Bayes' theorem, permutations and combinations, random variables, probability distribution functions, and a quiz. Lectures include: Introduction to Basic Probability, Introduction to Set Theory, Introduction to Conditional Probability, Introduction to Bayes Theorem, Introduction to Permutations and Combinations, Introduction to Random Variables, Introduction to Probability Distribution Functions, and a Quiz on Basic and Conditional Probability.
Introduction to Inferential Statistics
This section covers inferential statistics, including the normal distribution, skewness, kurtosis, statistical transformations, sample and population means, the central limit theorem, bias and variance, maximum likelihood estimation, confidence intervals, correlations, sampling methods, and a quiz. Lectures include: Introduction to Normal Distribution, Introduction to Skewness and Kurtosis, Introduction to Statistical Transformations, Introduction to Sample and Population Mean, Introduction to Central Limit Theorem, Introduction to Bias and Variance, Introduction to Maximum Likelihood Estimation, Introduction to Confidence Intervals, Introduction to Correlations, Introduction to Sampling Methods, and a Quiz on Inferential Statistics.
Introduction to Hypothesis Testing
This section covers hypothesis testing, including fundamentals, t-tests, z-tests, chi-squared tests, ANOVA tests, and a quiz. Lectures include: Fundamentals of Hypothesis Testing, Introduction to T Tests, Introduction to Z Tests, Introduction to Chi Squared Tests, Introduction to Anova Tests, and a Quiz on Hypothesis Testing.
Data Analysis and Data Viz : Introduction to Numpy and Pandas
This section introduces NumPy and Pandas for data analysis and visualization. Lectures include introductions to NumPy arrays and operations, Pandas, series and dataframes, reading CSV and JSON data using Pandas, data analysis using Pandas, and a quiz. Lectures include: Introduction to Numpy Arrays, Introduction to Numpy Operations, Introduction to Pandas, Introduction to Series and DataFrames, Reading CSV and JSON Data using Pandas, Analyzing the Data using Pandas, and a Quiz on Introduction to Numpy and Pandas.
Advanced Functions in Pandas
Explore advanced Pandas functions for data manipulation and analysis. The section includes lectures on indexing, selecting, and filtering data; merging and concatenation; correlation and plotting; lambda, map, and apply functions; grouping operations; cross-tabulation; filtering operations; interactive grouping and filtering; and a quiz. Lectures include: Indexing, Selecting, and Filtering Data, Merging and Concatenation using Pandas, Correlation and Plotting using Pandas, Introduction to Lambda, Map and Apply Functions, Introduction to Grouping Operations using Pandas, Introduction to Cross Tabulation using Pandas, Introduction to Filtering Operations using Pandas, Interactive Grouping and Filtering Operations, and a Quiz on Advanced Functions in Pandas.
Types of Charts and Visualizations
This section covers various chart types and data visualization techniques, including factors for good data visualization, univariate, bivariate, and multivariate visualizations, heatmaps, pairplots, and a quiz. Lectures include: Factors for good Data Visualization, Introduction to Univariate Data Visualizations, Introduction to Bivariate Data Visualizations, Plotting two Categorical Variables, Introduction to Multivariate Data Visualizations, Introduction to Heatmaps and Pairplots, and a Quiz on Types of Charts and Visualizations.
Advanced Data Visualizations
Learn advanced data visualization techniques, including colorscales, facet grids, subplots, 3D visualizations, interactive visualizations using Plotly, maps, funnel and Gantt charts using Plotly, animated visualizations using Plotly, and a quiz. Lectures include: Colorscales, Facet Grids, and Sub plots, Introduction to 3D Data Visualization, Introduction to Interactive Data Visualization, Introduction to Maps using Plotly, Introduction to Funnel and Gantt Charts using Plotly, Introduction to Animated Data Visualizations using Plotly, and a Quiz on Advanced Data Visualizations.
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