Your roadmap · आपका रोडमैप
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Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
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Part 1 of the syllabusपाठ्यक्रम भाग 1Unit-wise coaching with notes and doubt classes.
Unit 1: Introduction to Data ScienceUnit 2: Data Handling with NumPy and PandasUnit 3: Statistics and Visualization
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Machine Learning FundamentalsUnit 5: Model Evaluation and Applications
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 6 exam (university / board)विश्वविद्यालय / बोर्ड परीक्षाPrevious papers and full revision before the exam.
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Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
Syllabus · पाठ्यक्रम
1 subject · 5 chapters · 30 topics
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 this course
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
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
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.
