Data Science and Machine Learning (Diploma)

Data handling, visualization, statistics and basic machine learning algorithms.

6 MonthsDuration 5Chapters30Topics

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Syllabus · पाठ्यक्रम

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01Unit 1: Introduction to Data Science 6 topics

What is data science
Data science life cycle
Structured, semi-structured and unstructured data
Sources of data - CSV, Excel, databases, APIs
Tools - Python, Jupyter, Excel, Power BI
Applications and careers

02Unit 2: Data Handling with NumPy and Pandas 6 topics

NumPy arrays and operations
Pandas Series and DataFrame
Reading CSV and Excel files
Selecting, filtering and sorting data
Handling missing values and duplicates
Grouping and aggregation

03Unit 3: Statistics and Visualization 6 topics

Mean, median, mode
Range, variance and standard deviation
Correlation
Normal distribution basics
Line, bar, histogram, scatter and box plots
Exploratory data analysis

04Unit 4: Machine Learning Fundamentals 6 topics

Types of ML - supervised, unsupervised, reinforcement
Features, labels, training and test sets
Linear regression
Logistic regression and classification
k-Nearest Neighbours and decision tree
k-means clustering

05Unit 5: Model Evaluation and Applications 6 topics

Train-test split and cross-validation
Overfitting and underfitting
Confusion matrix, accuracy, precision, recall
Mean squared error
scikit-learn workflow
Mini case study - house price or student result prediction

About

You will be able to

Explain the data science process and the role of a data analyst / data scientist
Load, clean and transform data using Python, NumPy and Pandas
Calculate and interpret descriptive statistics such as mean, median, mode and standard deviation
Create charts with Matplotlib / Seaborn to explore data
Train and evaluate basic machine learning models - linear regression, logistic regression, k-NN, decision tree, k-means
Use scikit-learn to split data, train models and measure accuracy, precision and recall

Who it is for

Diploma Computer Science & Engineering students of Semester 6 (BTEUP)
Diploma IT students and beginners starting a data science career

What you need

Python Programming (Semester 5) and school-level statistics

Questions

Which syllabus is followed?

The BTEUP Diploma Computer Science & Engineering Semester 6 Data Science and Machine Learning syllabus. Where your college syllabus differs, the centre follows your own syllabus.

Is advanced mathematics needed?

No. Basic statistics and algebra are enough; formulas are explained with small solved examples.

Which software is used?

Python with Jupyter Notebook / Google Colab, NumPy, Pandas, Matplotlib and scikit-learn - all free.