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Master Data Analysis Python with Adaptive AI.

Master Data Analysis Python across 19 topics - Foundations, Intermediate, and Advanced - guided by Leyaa's adaptive intelligence.

3Parts
19Topics
158Patterns
40hEst. duration
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Data Analysis Python
Data Analysis Python Course
3 - part learning journey
Beginner
Foundations
6 topics · 51 patterns
Beginner
Intermediate
Intermediate
7 topics · 60 patterns
Intermediate
Advanced
Advanced
6 topics · 47 patterns
Advanced
Begin Part 1: Foundations
✦ Course Structure

Your Data Analysis Python Journey in 3 Parts

Each part builds on the last - a deliberate progression from beginner to advanced.

Beginner
Beginner
Part 1: Foundations
Covers Data Analysis Basics and Python Setup, NumPy Foundations, Pandas Series and 3 more topics.
6Topics
51Patterns
View topics
Intermediate
Intermediate
Part 2: Intermediate
Covers Data Cleaning, Data Transformation, Grouping and Aggregation and 4 more topics.
7Topics
60Patterns
View topics
Advanced
Advanced
Part 3: Advanced
Covers Advanced Pandas Operations, Text Data Analysis, Statistical Analysis in Python and 3 more topics.
6Topics
47Patterns
View topics
Curriculum

Everything You'll Learn

Explore every topic and pattern in the course.

Data Analysis Python

3 parts · 19 topics · 158 patterns
Beginner
Part 1: Foundations6 topics · 51 patterns
Beginner
01
Data Analysis Basics and Python Setup
7 patterns
Why Python is the top choice for data analysis
Data analysis workflow (collect, clean, explore, visualize, conclude)
Python vs R vs Excel for analysis
Jupyter Notebook setup and usage
Google Colab as alternative
Essential libraries overview (Pandas, NumPy, Matplotlib)
First data analysis walkthrough
02
NumPy Foundations
9 patterns
Why NumPy is the numerical backbone
Array creation (array, arange, linspace)
Array shapes and dimensions
Array indexing and slicing
Boolean indexing
Array arithmetic (element-wise)
Broadcasting rules
Aggregation functions (sum, mean, std)
Reshaping and transposing
03
Pandas Series
7 patterns
Why Series is the 1D data structure
Series creation from lists and dicts
Series indexing and selection
Series arithmetic and alignment
String methods on Series
Series sorting
Handling missing values in Series
04
Pandas DataFrame Basics
9 patterns
Why DataFrame is the core data structure
Creating DataFrames (dict, list, CSV)
Reading CSV files (read_csv)
DataFrame structure (index, columns, values)
Selecting columns
Selecting rows (loc, iloc)
Boolean filtering
Adding and removing columns
Basic DataFrame info (shape, dtypes, describe)
05
Data Loading and Export
9 patterns
Why flexible I/O handles real-world data
Reading CSV with options (sep, header, encoding)
Reading Excel files (read_excel)
Reading JSON (read_json)
Reading from SQL databases
Reading HTML tables
Exporting to CSV
Exporting to Excel
Exporting to JSON
06
Data Inspection and Summary
10 patterns
Why exploratory inspection guides analysis
head() and tail()
info() for column types
describe() for statistics
value_counts() for distributions
nunique() for cardinality
Checking data types
Memory usage analysis
Sample() for random rows
Correlation with corr()
Intermediate
Part 2: Intermediate7 topics · 60 patterns
Intermediate
01
Data Cleaning
10 patterns
Why data cleaning consumes most analysis time
Identifying missing values (isnull, isna)
Dropping missing values (dropna)
Filling missing values (fillna)
Forward fill and backward fill
Interpolation for missing numerics
Removing duplicates (drop_duplicates)
Renaming columns
Changing data types (astype)
String cleaning (strip, lower, replace)
02
Data Transformation
8 patterns
Why transformation reshapes data for analysis
apply() function for custom logic
map() for element-wise mapping
replace() for value substitution
cut() and qcut() for binning
Encoding categorical variables
Scaling and normalization concepts
Log transformation for skewed data
03
Grouping and Aggregation
9 patterns
Why groupby summarizes data by category
groupby() basics
Single and multiple column grouping
Aggregation functions (sum, mean, count)
agg() for multiple aggregations
transform() for group-level operations
filter() for group-level filtering
Pivot tables with pivot_table()
Cross-tabulation with crosstab()
04
Merging and Joining Data
8 patterns
Why combining datasets creates complete pictures
concat() for stacking DataFrames
merge() for SQL-style joins
Inner join
Left and right joins
Outer join
Merging on multiple keys
Handling duplicate column names
05
Date and Time Analysis
8 patterns
Why time-based analysis reveals trends
to_datetime() conversion
Extracting date components (year, month, day)
Date arithmetic (Timedelta)
Resampling time series
Rolling window calculations
Shift and lag operations
Date-based indexing and slicing
06
Data Visualization with Matplotlib
9 patterns
Why visualization communicates findings
Figure and axes creation
Line plots
Bar charts
Histograms
Scatter plots
Subplots for multiple charts
Labels, titles, and legends
Saving figures
07
Data Visualization with Seaborn
8 patterns
Why Seaborn creates statistical visualizations
Distribution plots (histplot, kdeplot)
Categorical plots (boxplot, violinplot)
Scatter plots with regression (regplot)
Heatmaps for correlation
Pair plots for feature relationships
FacetGrid for multi-panel views
Styling and themes
Advanced
Part 3: Advanced6 topics · 47 patterns
Advanced
01
Advanced Pandas Operations
8 patterns
Why advanced operations handle complex data
MultiIndex (hierarchical indexing)
Stack and unstack
Melt for wide-to-long reshaping
Pipe for method chaining
Window functions (expanding, ewm)
Sparse data handling
Categorical data type optimization
02
Text Data Analysis
7 patterns
Why text data requires special handling
String accessor (.str) methods
Pattern matching with str.contains
Extracting with str.extract (regex)
Tokenization basics
Word frequency analysis
Text cleaning pipeline
03
Statistical Analysis in Python
8 patterns
Why statistics validates hypotheses
Descriptive statistics review
t-test with scipy.stats
Chi-squared test
Correlation analysis (Pearson, Spearman)
Linear regression basics
ANOVA
P-values and significance
04
Feature Engineering
8 patterns
Why engineered features improve analysis
Creating interaction features
Polynomial features
Binning continuous variables
One-hot encoding
Label encoding
Date feature extraction
Aggregation-based features
05
Real-World Analysis Projects
7 patterns
Why project-based learning cements skills
Exploratory Data Analysis (EDA) template
Sales data analysis pattern
Customer segmentation pattern
Survey data analysis pattern
Financial data analysis pattern
Web analytics data pattern
06
Performance and Best Practices
9 patterns
Why efficiency matters with large datasets
Chunked reading for large files
Data type optimization
Vectorized operations vs loops
Memory-efficient operations
Profiling with line_profiler
Jupyter Notebook best practices
Reproducible analysis patterns
Report generation (notebooks to HTML/PDF)
Why Leyaa

Not just a course. A learning intelligence.

Leyaa doesn't just deliver content - it understands where you are, what you need, and how to get you there fastest.

Adaptive Diagnostics
Adaptive Diagnostics
Leyaa identifies which of the 158 patterns you've mastered and which need work - before you waste time on what you already know.
Pattern-Level Precision
Pattern-Level Precision
Every pattern gets a verdict - Learn, Practice, or Skip - so your study time goes exactly where it counts.
Intelligent Pacing
Intelligent Pacing
Leyaa reads your velocity across the whole course and adapts your focus list dynamically, keeping you in the optimal learning zone.
Before & After Visibility
Before & After Visibility
Every assessment creates a snapshot. Retake anytime to see exactly how much you've grown - topic by topic, pattern by pattern.
Learner Autonomy
Learner Autonomy
Leyaa recommends, never restricts. Override any suggestion and explore at your own pace - the system tracks everything regardless.
Clear Learning Path
Clear Learning Path
Foundations → Intermediate → Advanced - a structured journey where each part sets you up perfectly for the next.
Data Analysis Python

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19 Topics
158 Patterns
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