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Data Science Course

Data Science

This course provides a comprehensive introduction to Data Science, covering essential concepts, tools, and techniques. Students will learn how to collect, analyze, and visualize data using Python and its libraries. The curriculum includes statistical analysis, machine learning algorithms, data preprocessing, and model evaluation. By the end of the course, learners will be equipped with the skills to tackle real-world data challenges and make data-driven decisions.

Learning Outcomes:

  • Upon Completing learners can proficiently manage the entire data lifecycle—from collecting, cleaning, and curating diverse datasets to applying statistical, computational, and machine learning techniques for analysis and predictive modeling.Equipped with strong programming skills (e.g., Python, R, SQL), they are capable of designing reproducible, ethical data-driven solutions while collaborating across teams. With a grounding in domain knowledge and ethical considerations.
Curriculum

Installation of Anaconda Prompt30:25🔒
Jupyter Notebook-An Overview25:40🔒
Shorcut Lkeys in Jupyter Notebook20:15🔒
Data Types in Python20:15🔒

Rules for Naming the Variables35:15🔒
List, Tuple, Set, Dictionary28:40🔒
"Introduction to Files and directories Introduction to the command prompt or terminal paths"22:30🔒
"Text files Reading from a text file Opening a file using `with`"18:25🔒

If, elif and else condition.30:25🔒
For and While Loop25:40🔒

Machine Learning Libraries30:25🔒
Numpy-Hands on25:40🔒

"Pandas-Hands on"45:30🔒

Learn how to explore, visualize, and extract insights from data42:30🔒
Data Visualization38:45🔒
Matplotlib-Hands on35:20🔒
Seaborn hands on28:15🔒

You need to think statistically and to speak the language of your data50:20🔒
Measures of Central Tendency35:45🔒
Measures of Dispersion42:30🔒
"IQR Statistics-Hands-On"42:30🔒

Classification, Regression, Fine-tuning your model50:20🔒
Supervised Learning35:45🔒
Unsupervised Learning35:45🔒
Linear Regression35:45🔒
"Metrics in Linear Regression Hands-on in Linear Regression35:45🔒

Logistic Regression50:20🔒
Metrics in Logistic Regression35:45🔒
Hands-on in Logistic Regression42:30🔒

Linear regression50:20🔒
Metrics for Linear regression35:45🔒

Introduction to Data Preprocessing50:20🔒
Standardizing Data35:45🔒
Exploratory Data Analysis42:30🔒
Missing Values42:30🔒
Outliers42:30🔒
"Standardization Mnormalization Feature Scaling and Selection42:30🔒

Decision Tree50:20🔒
Bagging35:45🔒
Boosting Random Forest42:30🔒

Neural Network50:20🔒

“Use data science packages, analysis, visualization, create model, extract pure data etc50:20🔒

Excel Functions50:20🔒
Conditional Formatting35:45🔒
Pivot tables42:30🔒
Dashboards42:30🔒

Basics of database schema50:20🔒
Importance SQL Clauses35:45🔒
SQL Joins42:30🔒

Introduction to Power BI desktop50:20🔒
ETL pipeline in Power BI35:45🔒
Calculating fields with DAX42:30🔒
Visualizing data with reports42:30🔒
AI functionalities of Power BI42:30🔒

How to Enroll

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Price

₹3500

Course Title

Data Science

Language

English

Certification

Yes - Industry Recognized

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FAQs

The course duration is of 2 months & some course may extend to 3 months

Yes at the end of the course completion you will get certificates.

ABCPanda team will arrange a doubt clearance session accordingly.

Yes, for Recorded sessions & Live sessions access would be 1 year

No our mentors will teach from basic. If you have experience, it would add an advantage.