Data Science Foundations: Excel, Python & Tableau for Beginners

What you will learn:

Master Excel for data analysis using pivot tables and charts.
Become proficient in Python for data manipulation with Pandas and NumPy.
Conduct statistical analysis and hypothesis testing.
Create stunning dashboards and interactive visualizations with Tableau.
Clean and prepare datasets effectively for analysis.
Gain a strong understanding of statistical concepts for data-driven decision-making.
Utilize powerful Python visualization libraries like Matplotlib and Seaborn.
Integrate Excel, Python, and Tableau into a complete data workflow.
Build confidence by applying real-world data analysis skills to practical projects.
Transition from beginner to confident data science professional.

Description

Unlock your data science potential with our comprehensive course designed for beginners! Learn to harness the power of Excel, Python, and Tableau to transform raw data into actionable insights. This practical course blends theoretical understanding with hands-on projects, ensuring you build a robust skillset.

We'll guide you through essential data manipulation techniques using Excel's powerful features, including pivot tables, functions, and data cleaning. You'll then dive into Python, mastering libraries like Pandas, NumPy, and Matplotlib for efficient data analysis and visualization. Finally, you'll discover the art of creating compelling dashboards and interactive visualizations using Tableau.

What you'll gain:

Proficiency in Excel's analytical tools and functions
Solid Python programming skills for data science
Data visualization mastery using Matplotlib, Seaborn, and Tableau
Statistical foundation for data-driven decision-making
Experience with real-world data analysis projects
Confidence to apply learned skills to various industries.

This course is your pathway to becoming a confident data professional. No prior experience needed – simply enroll now and begin your data science journey!

Curriculum

Excel Mastery

This section provides a thorough grounding in Excel for data analysis. You'll cover the Excel interface, learn essential functions like sorting, filtering, and conditional formatting. The module also delves into statistical and mathematical functions, lookup functions (including INDEX and MATCH), pivot tables and charts, logical and text functions, date and time functions, and advanced features like Power Query, Scenario Manager, Goal Seek, Solver, and data visualization best practices. Each topic includes interactive exercises and quizzes to reinforce learning.

Tableau Data Visualization

This section introduces Tableau, a powerful data visualization tool. You will explore different Tableau versions and their features, learn about dimensions and measures, and master the creation of various chart types including bar charts, line charts, pie charts, bubble charts, heatmaps, treemaps, area charts, dual-axis charts, scatter plots, bullet charts, waterfall charts, and Gantt charts. The section ensures hands-on practice to build your visualization skills.

Python for Data Science

This section focuses on Python programming for data analysis. You'll learn foundational Python concepts: variables, data types, operators, lists, tuples, sets, dictionaries, stacks, queues, space and time complexity, sorting and searching algorithms, and functions. You'll then move on to advanced topics like string manipulation (including regular expressions), conditional statements, loops, object-oriented programming concepts (OOPs), date and time manipulation, and analytical and aggregate functions. Quizzes throughout the section help reinforce your understanding.

Statistics for Data Science

This module introduces essential statistical concepts for data science. Beginning with the importance of statistics in data analysis, it covers types of data, measures of central tendency and spread, dependence and shape, standard scores, descriptive and inferential statistics. It also covers basic and conditional probability, including Bayes Theorem, permutations, and combinations. The module further explores normal distribution, skewness, kurtosis, sampling methods, hypothesis testing, t-tests, z-tests, chi-squared tests, and ANOVA tests.

Data Analysis & Visualization with Python

Building upon prior Python knowledge, this section delves into using NumPy and Pandas for data manipulation. You'll master data cleaning, reading data (CSV and JSON), data analysis, selecting, filtering, merging, and concatenating datasets. You'll then learn advanced Pandas techniques, including lambda, map, and apply functions, grouping operations, cross-tabulation, and filtering. The section concludes with creating data visualizations using Python libraries like Matplotlib and Seaborn, including univariate, bivariate, and multivariate visualizations, heatmaps, pairplots, and advanced techniques like animated visualizations.

Data Cleaning and Preprocessing

This section focuses on handling missing values and outliers in datasets. You will learn various techniques for identifying, dealing with, and imputing missing values, alongside methods for dealing with outliers and their effects on machine learning models.

Categorical Encoding

This short but important module covers various categorical encoding techniques crucial for preparing data for machine learning algorithms. This includes Label encoding, ordinal encoding, binary encoding, baseN encoding, and target encoding. The practical application of each technique will be demonstrated.

Data Manipulation Functions in Pandas

This section enhances your Pandas skills further by covering functions like reindex, set_index, reset_index, sort_index, replace, droplevel, stack, unstack, melt, explode, and squeeze – demonstrating their applications and practical use in data manipulation.

Feature Engineering

This section teaches advanced techniques in feature engineering, essential for building predictive models. You will learn techniques to drop unnecessary columns, decompose date and time features, decompose categorical features, binning of numerical features, and aggregation of features.

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