Data Science with Python
Instructor
What Will You Learn?
About This Course
A career in Data Science requires a combination of technical and analytical skills, including proficiency in programming languages such as Python and Tableau, expertise in statistical analysis and data visualization tools, and techniques. Introduction to Data Science Course is a comprehensive program designed to give students a thorough understanding of Data Science concepts and techniques. Taught by Industry professionals, the 2-month introductory course, helps students develop the skills needed to become proficient in data analysis, machine learning, and data visualization, ensuring they get expert guidance and mentorship throughout the course.
Requirements
FAQ
Check the frequently asked questions about this course.
Curriculum Overview
This course includes 2 modules, 21 lessons, and 20:00 hours of materials.
Topic : Core Types
Detailed Concepts Covered : Numbers, Strings, Lists, Tuples, Sets, Dictionaries, Mutability, Indexing, Slicing
Topic : Conditionals & Loops
Detailed Concepts Covered : If/elif/else, Logical Operators, Nested Conditions, Common Patterns, For/While Loops, Break/Continue/Pass
Topic : Modularity & Exceptions
Detailed Concepts Covered : User-defined Functions, Arguments & Return, Recursion, Try/Except/Finally, File Handling Basics
Topic : Iteration Patterns
Detailed Concepts Covered : List Comprehensions, Dict Comprehensions, Set Comprehensions, Loop Refactoring, Common Python Patterns
Topic : Arrays & Vectorization
Detailed Concepts Covered : Ndarray Creation, Indexing & Slicing, Reshape, Broadcasting, Vectorized Operations
Topic : DataFrames & Cleaning
Detailed Concepts Covered : Series & DataFrame creation, Indexing & Selection, Filtering, Handling Missing Data, Derived Columns
Topic : Joins & Aggregations
Detailed Concepts Covered : Merge, Concat, Groupby, Aggregations, Pivot/Tidy Data Patterns, Basic Statistics
Topic : Matplotlib Basics
Detailed Concepts Covered : Line, Bar, Scatter, Histogram, Box Plots, Styling, Labels, Legends, Annotations
Topic : Seaborn & Review
Detailed Concepts Covered : Seaborn Plots (Distplot, Pairplot, Heatmap), Customizations, Layouts, Review Python & Data Concepts
Topic : Relational Querying
Detailed Concepts Covered : SELECT/WHERE/ORDER BY, JOIN/GROUP BY/HAVING, aggregates
Topic : Pipelines & Windows
Detailed Concepts Covered : Method chaining, window ops, pivot/melt, categorical encoding
Topic : Descriptive → Inferential
Detailed Concepts Covered : Distributions, CLT intuition, hypothesis testing overview
Topic : Transformations : Detailed Concepts Covered : Scaling, encoding, binning, datetime features, text features
Topic : SLR/MLR
Detailed Concepts Covered : Assumptions, train/evaluate, R²/RMSE, residual diagnostics
Topic : Logistic + Metrics
Detailed Concepts Covered : Precision/recall/F1, ROC/AUC, calibration basics
Topic : K-Means + PCA
Detailed Concepts Covered : Elbow & silhouette, PCA explained variance, visualization
Topic : Classical Text Pipeline
Detailed Concepts Covered : Tokenization, stopwords, TF-IDF, Naïve Bayes sentiment
Topic : Temporal Patterns
Detailed Concepts Covered : Rolling mean, ACF/PACF intuition, ARIMA overview
Topic : Communicating Insights
Detailed Concepts Covered : Tableau/Power BI dashboarding, Streamlit quick app
Hi everyone
With this attachment, we have 10 projects available.
Please complete any 2 projects from the list and submit them as per the instructions shared.
If you have any questions or need clarification, feel free to reach out.
Thank you.
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