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Data Analysis Bootcamp: Master Data Science Skills
Rating: 4.5 out of 5(409 ratings)
33,442 students

Data Analysis Bootcamp: Master Data Science Skills

Learn Data Analysis With Python, Jupyter, Pandas, Dropna - Learn Data Cleaning, Visualization, and Modeling
Created byLearnify IT
Last updated 4/2026
English
English [Auto],

What you'll learn

  • Understand key data concepts like data types, variables, and data cleaning techniques.
  • Master the powerful Python programming language for data manipulation, analysis, and visualization.
  • Discover insightful patterns and trends in your data through exploratory data analysis.
  • Effectively communicate your findings through compelling data visualizations and reports.

Course content

1 section • 17 lectures • 6h 30m total length

  • Introduction11:59

    Explore data analytics foundations, from data cleaning and interpretation to descriptive, diagnostic, predictive, and prescriptive analytics, using Python libraries, and later Excel or Google Slides for practical analysis.

  • Setup and Basic Data Analysis29:21

    Set up data analysis with Anaconda Navigator, install Python, and launch Jupyter Notebook to perform basic NumPy array operations, data visualization, and introductory analysis workflows.

  • More Basic Data Analysis Commands30:22

    Master basic data analysis commands in NumPy with Python, including random arrays, diag, ndim, size, mean, median, std, var, sum, min, max, sort, transpose, flatten.

  • Counting functions in Data Analysis new update11:26

    Explore counting functions in Python for data analysis using NumPy, including unique values, counts, intersections, unions, set differences, and set xor within a Jupyter notebook workflow.

  • Pandas & Pyplot24:33

    Learn to import pandas and pyplot, load a csv with read_csv, and explore data using head, tail, and describe. Visualize distributions with matplotlib and seaborn box plots and histograms.

  • Linear Regression & Heatmap40:39

    Explore linear regression and heat maps in Python to visualize correlation matrices and correlations, assess multicollinearity, and forecast targets using Seaborn, Matplotlib, and scikit-learn.

  • Dropna21:17

    Master dropna in pandas to remove rows or columns with nan values, understand its advantages and disadvantages, and follow hands-on notebook examples for practical data cleaning.

  • PD.Pivot Table24:13

    Learn to use Pandas pivot_table to reformat and summarize data with multi-level indexing, aggregating by product and region to reveal total units and mean profit.

  • DF.Rolling() new update17:59
  • PD.Merge() and PD.Concat()28:15

    Explore PD.merge and PD.concat in pandas to merge and concatenate data frames, compare SQL like joins, handle keys and indices, and build integrated datasets with practical Python examples.

  • PD.Cut() and PD.Gcut()27:23

    Learn how pd.cut and pd.qcut discretize continuous data into bins, compare equal-width and quantile-based bins, and apply them in a practical Jupyter Notebook with sample data.

  • PD.Resample()24:33

    Explore how to use pandas resample to change time series frequency in Python. Learn about aggregation options, missing value handling, and the method's advantages and drawbacks.

  • Scikit-Learn Pipeline27:21

    Learn how the scikit-learn pipeline automates data pre-processing, feature selection, and model training into a reproducible workflow while reducing data leakage and supporting gridsearchcv and randomizedsearchcv.

  • PD.Merge() and PD.Concat()28:15

    Learn how pd.merge and pd.concat merge and concatenate pandas dataframes, with SQL-like joins, key matching, and practical steps for combining datasets in Python.

  • Data.sort_values(by=_column_) part 117:29

    Learn to sort a pandas DataFrame by one or more columns using sort_values, with examples by country and score, and compare sorted results to the original data frame.

  • Class Project 112:01

    Reshape a wide data frame into a long format with pandas melt, keeping product as id var and turning quarterly sales into quarter and sales columns for analysis and visualization.

  • Class Project 213:19

    Create scatter plots in Pandas using the data plot scatter xy function, preparing a two-column data frame, plotting age versus income, and analyzing correlation.

Requirements

  • No experience required

Description

Are you ready to embark on a data-driven journey? This bootcamp is your first step towards becoming a skilled data analyst. Whether you're a beginner or looking to enhance your data skills, this course is designed to provide you with a solid foundation in data analysis.

In this course, you'll learn:

  • Data Fundamentals:

    • Understand key data concepts like data types, variables, and data cleaning techniques.

    • Learn how to handle missing data and outliers.

  • Data Analysis with Python:

    • Master the powerful Python programming language for data manipulation, analysis, and visualization.

    • Utilize libraries like Pandas to efficiently work with data.

  • Data Exploration and Visualization:

    • Discover insightful patterns and trends in your data through exploratory data analysis.

    • Create visually appealing data visualizations using various chart types (histograms, bar charts, scatter plots, etc.).

  • Statistical Analysis:

    • Apply statistical methods to draw meaningful conclusions from your data.

    • Understand hypothesis testing, correlation analysis, and regression analysis.

  • Data Storytelling:

    • Effectively communicate your findings through compelling data visualizations and reports.

    • Present your insights in a clear and concise manner to a non-technical audience.


By the end of this course, you'll be able to:

  • Clean and prepare data for analysis

  • Perform exploratory data analysis to uncover insights

  • Visualize data effectively to communicate findings

  • Apply statistical techniques to draw meaningful conclusions

  • Use Python to automate data analysis tasks

  • Create compelling data stories to drive decision-making


What You'll Get:

  • Lifetime Access to Course Content: Learn at your own pace, anytime, anywhere.

  • High-Quality Video Lectures: Clear and concise explanations of each topic.

  • Practical Exercises: Apply what you've learned with hands-on projects.

  • Certificate of Completion: Showcase your new skills to potential employers.

No prior programming experience is required. This course is designed for beginners and assumes no prior knowledge of data analysis or Python. Enroll now and start your data analysis journey today!

Who this course is for:

  • Anyone who looking to learn data analysis

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