Data Analyst in Python, Tableau, SQL & ChatGPT with Projects
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Are you interested in starting a career in data analytics but have no experience? Become proficient in essential tools such as Python, SQL, Tableau, and ChatGPT with this beginner data analyst course, designed to prepare you with the skills necessary to tackle real-world challenges as a data analyst. Whether you are looking for a career change to a data analyst, or have some experience and want to build on it, this step by step course will help guide you.
What You Will Learn:
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Python for Data Analysis: Start from the basics and advance to complex data manipulations with Pandas and Matplotlib.
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SQL: Learn to manage and query databases efficiently, from simple commands to complex queries.
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ChatGPT for Productivity: Enhance your coding and analysis efficiency with optimized ChatGPT prompts.
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Tableau for Visualization: Create impactful visualizations and prepare to become a Tableau Certified Data Analyst.
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Hands-On Projects: This is a project-based data analyst course – from analyzing LA crime statistics to exploring sales trends, apply what you’ve learned in practical projects.
Why This Course?
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Comprehensive & Practical: Gain hands-on experience with real-world projects that prepare you for the job market.
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Accessible to Beginners: No prior experience? No problem! We cater to all skill levels.
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Flexible Learning: Study at your pace with lifetime access to all course materials.
Who Should Enroll?
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Aspiring data analysts, career switchers, and professionals aiming to upskill in data analysis and visualization.
If you’re ready to start your journey in data analysis, enroll now and begin transforming data into insights!
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Download AnacondaLearn how to download and install Anaconda, a popular distribution for Python and R, which includes Jupyter Notebook and essential packages for data science.
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Introduction to Jupyter Notebook
Get introduced to Jupyter Notebook, an interactive web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text.
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Import Pandas and Matplotlib
Understand how to import essential Python libraries like Pandas and Matplotlib, which are fundamental for data manipulation and visualization.
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Loading CSV Files into Jupyter Notebook
Learn how to load CSV files into Jupyter Notebook, a crucial step for data analysis, and explore various methods for importing data.
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Markdown and Commenting Out Code
Discover the use of Markdown for formatting text and learn how to comment out code in Jupyter Notebook to enhance readability and documentation.
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Introducing Our Datasets
Get an overview of the datasets you will be working with, understanding their structure, and the type of data they contain.
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Essential Python Methods to Understand Datasets
Explore essential Python methods that help you understand and analyze your datasets, laying the foundation for effective data analysis.
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Renaming Columns in Pandas
Learn how to rename columns in Pandas DataFrames to make your data more readable and easier to work with.
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Selecting Specific Columns
Understand how to select specific columns in a DataFrame, allowing you to focus on the most relevant data for your analysis.
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Performing Basic Calculations
Perform basic calculations on your data, such as mean, max, min, and sum, to derive meaningful insights.
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Understanding Standard Deviation and Count
Learn about standard deviation and count, two important statistical measures, and how to compute them using Pandas.
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Descriptive Statistics with the Describe Method
Use the describe method in Pandas to generate descriptive statistics, summarizing the central tendency, dispersion, and shape of your dataset’s distribution.
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Analyzing Frequency with Value Counts
Analyze the frequency of unique values in your data using the value_counts method, helping you understand data distribution and identify patterns.
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Pandas Part One Quiz
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Working with Indexes in Pandas
Discover how to work with indexes in Pandas DataFrames.
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Sorting Values in DataFrames
Learn how to sort values in DataFrames based on different criteria, enabling you to organize and analyze your data more effectively.
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Accessing Data with Loc and Iloc
Understand how to access specific data points in your DataFrames using loc and iloc.
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Advanced Indexing: Multi-Indexes and Slices
Explore advanced indexing techniques such as multi-indexes and slices, which allow for more complex data manipulations.
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From DataFrames to Plots
Transition from data manipulation to data visualization by learning how to create plots from DataFrames using Matplotlib.
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Pandas Part Two Quiz
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Handling Missing Values: Dealing with NULLs
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Unique and Nunique Methods
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Filtering Data with the Between Method
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Applying Multiple Filter Conditions
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Advanced Filtering with Isin and Or
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Understanding the Tilde (~) Operator for Bitwise Negation
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Creating and Dropping Columns in DataFrames
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Generating Dynamic Columns
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Pandas Part Three Quiz
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Introduction to Matplotlib
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Creating Bar Plots and Adding Annotations
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Visualize Data Distribution with Histograms
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Display Data with Pie Charts
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Explore Relationships with Scatter Plots
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Scatter Plots using World Happiness Dataset
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Visualize Trends with Line Plots
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Structuring Visualizations with Figures
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Organize Plots using Subplot
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Visualizing Sales Data - Part One
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Visualizing Sales Data - Part Two
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Formatting Sales Plots - Part One
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Formatting Sales Plots - Part Two
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Getting Dataset and Importing to Jupyter Notebook
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Understand and Prepare Data - Part One
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Understand and Prepare Data - Part Two
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Feature Understanding & Analysis - Part One
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Feature Understanding & Analysis - Part Two
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Feature Understanding & Analysis - Part Three
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Time Taken to Report Crime
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Average Victim Age per Crime
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Areas with Most Crime
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Crime Count by Time of Day
