DA-VISUALIZE.AW1
Hands-on Data Analysis and Visualization with Pandas
It’s time for an upgrade. Learn Pandas once and start commanding those datasets.
- 12 Interactive Lessons and 104 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
12Interactive Lessons
104Topics
01 / Skills you'll get
What you will be able to do
Enroll in our data analysis with Python course and wield JupyterLab, Pandas, and Seaborn to dissect data.
This course cracks open Python’s data ecosystem: clean messy datasets with Pandas, run statistics with SciPy, and visualize trends with Matplotlib/Seaborn. You’ll optimize memory for large datasets, merge time series, and even automate ETL.
By the end of this course, you’ll transform raw data into clear, actionable insights. Data waits for no one. Start now.
- Write and execute Python code in JupyterLab for data analysis.
- Manipulate numerical data at scale using NumPy arrays and advanced operations.
- Clean, transform, and merge complex datasets with Pandas DataFrames.
- Perform time series analysis to identify trends, seasonality, and anomalies.
- Apply statistical methods for hypothesis testing and data validation.
- Create publication-quality visualizations using Matplotlib and Seaborn.
- Conduct end-to-end exploratory data analysis on real-world datasets.
Course Highlights
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12 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
12 Interactive Lessons · 104 topics01 Preface +
02 Introduction to Data Analysis 9 topics +
- Inspiration for data analysis
- Domain expertise
- Maths and statistics
- Artificial intelligence
- Machine learning
- Data Infrastructure
- Data Analysis Process
- Why Python for Data Analysis?
- Conclusion
03 JupyterLab 7 topics +
- Introduction to JupyterLab
- Components
- Cell modes
- Menu
- Magic commands
- Keyboard shortcuts
- Conclusion
04 Python Overview 8 topics +
- Python, Hello World
- Variables and data types
- Functions
- Lambda
- List comprehensions
- Functional programming using (map, filter, and reduce)
- Working with datetime objects
- Conclusion
05 Introduction to Numpy 15 topics +
- Ndarray
- Difference between List and Numpy arrays
- Storage
- Type check
- Speed
- Copying arrays
- Mathematical operations
- Trigonometric functions
- Statistical operations
- Reshaping
- Vertical and horizontal stacking of Numpy arrays
- Fancy indexing
- Indexing with Boolean arrays
- Broadcasting
- Conclusion
06 Introduction to Pandas 4 topics +
- Data structures in pandas
- Series
- DataFrames
- Conclusion
07 Data Analysis 16 topics +
- Handling different file formats
- Handling rows and columns
- Groupby
- Filter
- Concatenate DataFrames
- Merge DataFrames
- Purging duplicate rows
- Data Transformations
- Crosstab
- Cleansing the Data
- Replacing individual values
- Pivot and pivot table
- Grouper
- Handling large datasets
- Modin Pandas
- Conclusion
08 Time Series Analysis 8 topics +
- Creating time series data
- Converting string-based dates to datetime objects
- Unix / Epoch time
- Time Series Analysis Using a Real-Time Dataset
- Handling Timezones
- Shifting or Lagging
- Handling Holidays
- Conclusion
09 Introduction to Statistics 7 topics +
- Population
- Sample
- Types of data
- Levels of Measurement
- Inferential Statistics
- Hypothesis Testing
- Conclusion
10 Matplotlib 10 topics +
- Why data visualization?
- Matplotlib architecture
- Chart properties
- Controlling xticks, y_ticks, and tick_labels
- Scatter plot
- Bar plot
- Histograms
- Pie Chart
- Subplots
- Conclusion
11 Seaborn 8 topics +
- Why Seaborn?
- Matplotlib versus Seaborn
- About pokemon
- Importing libraries and dataset
- Visualizing Statistical Relationships
- Plotting Categorical Variables
- Visualizing the Distribution of the Data
- Conclusion
12 Exploratory Data Analysis 12 topics +
- A little story, Titanic
- Importing libraries and dataset
- Handling missing values
- Variable identification
- Categorical nominal
- Univariate analysis
- Bivariate analysis
- HeatMap
- Multivariate Analysis
- Handling Outliers
- Feature Selection
- Conclusion
03 / FAQs
Questions before you start
Is Pandas good for data analysis?+
Yes! Pandas is the gold standard for data analysis in Python. It’s optimized for cleaning, transforming, and analyzing large datasets.
Is there any certification for Pandas?+
No standalone “Pandas certification” exists but this course includes a Python For Data Science certificate covering Pandas, NumPy, and visualization tools. It’s ideal for resumes.
How long will it take to learn Pandas?+
- Basics (1–2 weeks): DataFrames, filtering, basic operations.
- Intermediate (1 month): Merging datasets, time-series, stats.
- Advanced (2+ months): Optimization, large datasets, integration with ML.
This course condenses it to 4–6 weeks with focused practice.
Is this course suitable for beginners with no Python experience?+
This course covers Python Fundamentals, but basic programming knowledge (variables, loops, functions) is recommended for the best experience.
Will this course help me get a data science job?+
Absolutely! You’ll gain hands-on skills in data cleaning, analysis, and visualization, which are critical for entry-level data roles.
Keep going
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