Data Science Handwritten Notes PDF

Key takeaways

• Statistics numericals and short Pandas code snippets are easy marks.
• Learn the life cycle steps in order; it is a common opening question.
• Each section has exam tips and formula boxes, and the last page lists the most asked practice questions.
• These are original Uncodemy revision notes, not official university notes; check your own syllabus.

About these Data Science handwritten notes

This page offers Uncodemy's Data Science notes as a free handwritten-style PDF. Data Science is about turning raw data into useful answers through collection, cleaning, statistics, visualisation and models. The notes are written in short points with neat tables, labelled diagrams, boxed formulas and exam tips, so you can revise a full unit in one sitting instead of reading a long textbook chapter.

Please note: these are original Uncodemy revision notes. They follow the common syllabus that most Indian universities share for this subject, but they are not official university notes. Check your own university syllabus and add anything it lists that is missing here.

What's inside the Data Science notes

The PDF is arranged in 9 sections plus a final page of most asked practice questions. The sections follow the usual unit order of a Data Science syllabus, so you can map them to your own units quickly. Here is what each section covers.

Introduction to data science

What data science is, the data science life cycle, related fields such as statistics and ML, and descriptive, diagnostic, predictive and prescriptive analytics.

Data and data types

Structured, semi-structured and unstructured data, and nominal, ordinal, interval and ratio levels of measurement.

Descriptive statistics

Mean, median and mode, range, variance and standard deviation, and shape measures such as skewness and kurtosis.

Probability and inferential statistics

Probability basics, normal and other distributions, the Central Limit Theorem, hypothesis testing with p-values and Type I and II errors, and correlation.

Data wrangling

Handling missing values, duplicates and outliers, with Pandas and NumPy essentials for loading, cleaning and grouping data.

Exploratory data analysis and visualisation

The goals of EDA and which plot to use when: histogram, box plot, scatter plot, bar chart and heatmap.

Modelling overview

Supervised and unsupervised learning, train-test split, overfitting and the common evaluation metrics.

Big data and tools

The Vs of big data, Hadoop components (HDFS, MapReduce, YARN), Hadoop vs Spark, and common data science tools.

Ethics and communication

Privacy, bias and responsible use of data, and how to present findings clearly to non-technical people.

Most asked questions (practice)

Ten exam-style questions, from the life cycle to Pandas cleaning code.

How to use handwritten notes to revise Data Science

Data Science papers combine statistics, code and theory, so revise all three from the notes instead of only reading definitions.

• Compute mean, median, mode, variance and standard deviation for one small dataset by hand.
• Write the Pandas lines to load a CSV, fill missing values and group by a column, from memory.
• Match each EDA plot in the notes to one question it answers.
• Keep hypothesis testing steps and error types on one card for the last revision.

A simple three-pass method works well with handwritten notes. In the first pass, read one section and underline only what you do not already know. In the second pass, close the notes and write the definitions, diagrams and steps for that section on a blank sheet, then compare and correct in a different colour. In the third pass, a day or two later, answer two or three exam-style Data Science questions in timed conditions using only memory. Add your own examples and doubts in the margins so the PDF becomes your personal revision copy.

Formulas, diagrams and definitions to memorise

• The data science life cycle: problem, data collection, cleaning, EDA, modelling, evaluation, deployment and communication.
• Formulas for mean, variance and standard deviation, and the empirical rule for the normal distribution (68, 95 and 99.7 percent).
• Hypothesis testing steps: state H0 and H1, choose the significance level, compute the test statistic and p-value, decide.
• Type I error (rejecting a true H0) vs Type II error (failing to reject a false H0).
• Key Pandas calls: read_csv, isnull, fillna, dropna, groupby and merge.

Common mistakes that cost marks

• Saying that correlation proves causation.
• Using the mean to describe skewed data where the median is the better measure.
• Rejecting H0 when the p-value is greater than the significance level.
• Dropping all rows with missing values without explaining the impact on the data.

Exam focus: Data Science topics that carry the most marks

These Data Science topics carry the most marks in most university papers.

• The data science life cycle and types of analytics.
• Descriptive statistics numericals.
• Normal distribution, Central Limit Theorem and hypothesis testing.
• Data cleaning, EDA plots, Pandas code and Hadoop vs Spark.

Typical long questions

1. Define data science and explain the data science life cycle.
2. Compare descriptive, predictive and prescriptive analytics.
3. Explain nominal, ordinal, interval and ratio data with examples.
4. Find the mean, median, mode, variance and standard deviation of given data.
5. Explain hypothesis testing, p-value and Type I and Type II errors.
6. How do you handle missing values and outliers?
7. Explain Hadoop components and compare Hadoop with Spark.

Marks per question change between universities and years, so confirm the pattern on a recent official paper. In long answers, start with a definition, add a labelled diagram or table, explain the steps and finish with an example or comparison. Underline the key term in each answer and leave a line between steps, because examiners mark faster when the structure is easy to see.

A 7-day revision plan for Data Science

1. Day 1: Introduction to data science, Data and data types.
2. Day 2: Descriptive statistics, Probability and inferential statistics.
3. Day 3: Data wrangling, Exploratory data analysis and visualisation.
4. Day 4: Modelling overview, Big data and tools.
5. Day 5: Ethics and communication.
6. Day 6: redo the numericals and code: one descriptive statistics problem, one hypothesis test and one Pandas cleaning script.
7. Day 7: answer the most asked practice questions at the end of the notes from memory, then solve one official previous year paper in three hours and mark the units that cost you time.

Keep a single sheet of formulas, definitions and diagrams from the notes for the last hour before the exam, and do not start new topics on the final day.

Who these notes are for

• B.Tech CSE, IT and AI-DS students preparing for the semester Data Science exam.
• BCA, MCA and B.Sc students with a data science or data analytics paper.
• Beginners who want a short revision of statistics and Pandas before a data science course.
• Freshers revising basics before data analyst or data science interviews.

The notes are a revision aid, not a first textbook. If a Data Science topic is new to you, learn it from your prescribed book or class lectures first, then come back to the notes to compress and revise it. For practice, pair them with your university's official previous year papers. If your syllabus has a topic that is not covered here, you can ask for it through the handwritten notes request page linked below.

Download Data Science Handwritten Notes PDF

Sources and further reading

• pandas documentation
• NIST/SEMATECH e-Handbook of Statistical Methods
• AKTU: old question papers (official archive)