DATA SCIENTIST ROADMAP

2025-2026

Kickstart your Data Science career with this comprehensive 6-month end-to-end roadmap!

This detailed guide is designed to help you build Data Science skills from scratch and covers
everything you need, including:

1. Skills with resources

2. Hands-on projects
3. Essential soft skills
4. Resume Template with Tips, and an Interview Preparation Guide

Core Responsibilities of a Data Scientist:

1. Data Collection & Preprocessing - Gather and clean structured and unstructured data
from various sources, handling missing values, outliers, and inconsistencies to ensure
data quality.

2. Exploratory Data Analysis (EDA) - Analyze data distributions, identify patterns and
trends, and visualize insights using statistical techniques and data visualization tools.

3. Building Predictive Models - Develop and train machine learning models using
algorithms such as regression, classification, and clustering while optimizing
performance through hyperparameter tuning.

4. Feature Engineering & Selection - Identify, create, and select the most relevant
features to enhance model accuracy while reducing dimensionality using techniques like
PCA and feature selection methods.

5. Deployment & Monitoring - Identify, create, and select the most relevant features to
enhance model accuracy while reducing dimensionality using techniques like PCA and
feature selection methods.

6. Business & Stakeholder Communication - Translate data-driven insights into

actionable business recommendations, creating reports, dashboards, and presentations
for both technical and non-technical audiences.

7. Staying Updated with Industry Trends - Keep up with advancements in AI, machine
learning, and big data by exploring new tools, techniques, and best practices to enhance
data science capabilities.
Basic Requirements for becoming a Data Scientist:
1. A minimum of bachelor’s degree
2. Technical Skills
3. Soft Skills
4. Domain Knowledge
5. Relevant Coursework
6. Certifications

Salary & Career Graph:

Career Graph Experience Job Titles Average Salary

(INR per annum)
Entry - Level 0-2 years Junior Data Scientist, Data ₹6 - ₹10 LPA
Analyst
Mid-Level 2-5 years Data Scientist, Machine ₹12 - ₹20 LPA
Learning Engineer
Senior- Level 5-8 years Senior Data Scientist, AI ₹20 - ₹35 LPA
Engineer
Lead/ Managerial Role 8-12 years Lead Data Scientist, Data ₹35 - ₹50 LPA
Science Manager
Executive Level 12+ years Chief Data Scientist, Director of ₹50 LPA+
Data Science

* Note: The minimum and maximum salary ranges vary depending on the type of company. Product companies
typically offer 25-50% higher salaries compared to service-based or consulting companies*
 Roadmap to Landing a New Role
Data Scientist

PHASE 1 : UPSKILLING

Technical Skills Coursework Other Skills Certifications

-Python - Linear Algebra -Case Studies -IBM Data Science

-SQL - Probability & Statistics -Behavioural QnA -AWS ML
-Machine Learning - Calculus -Business Storytelling -MS Azure AI
-Deep Learning - Hypothesis Testing -Communication & - Coursers AI/ ML
-Data Visualisation - A/B Testing Presentation Specialisations
-Big Data Tools - Optimisation Techniques

PHASE 2 : JOB SEARCH

Hands-on Resume LinkedIn / Apply for Jobs Interview

Project Building Naukri
Optimisation
Overview of Estimated Time to Prepare

Skill Estimated Time Learning Phase

Programming (Python) 1-2 months Beginner
Version Control (Git) 1-2 weeks Beginner
Data Structures & 1-2 months Beginner
Algorithms
SQL 1-2 months Beginner
Mathematics & 2-3 months Beginner
Statistics
Data Collection & 1-2 months Beginner
Visualisation
Machine Learning 2-3 months Intermediate
Fundamentals
Deep Learning Intermediate
2-3 months
Specialisation (NLP or 2-3 months Advanced
Computer Vision) 2-3 months
Big Data (Optional) 2-3 months Advanced

* Keep in mind that the time needed to learn each skill can vary for everyone. These
estimates are based on dedicating 3 to 5 hours of study every day.
 PHASE 1 :
UPSKILLING
 1. TECHNICAL SKILLS

Month 1: Python Programming & Statistics

Week 1-2 : Python Fundamentals

• Day 1 - 2: Introduction to Python syntax, variables, and data types

https://www.youtube.com/watch?v=rfscVS0vtbw

• Day 3 - 4: Control structures – loops and conditionals

https://www.youtube.com/watch?v=Z0b2xG3jpyM

• Day 5 - 6: Functions & Modules

https://www.youtube.com/watch?v=9Os0o3wzS_I

• Day 7 - 8: Data structures – lists, tuples, dictionaries, and sets

https://www.youtube.com/watch?v=R-HLU9Fl5ug

• Day 9 - 10 : File handling and exceptions

https://www.youtube.com/watch?v=Uh2ebFW8OYM

• Day 11 - 12: Object-Oriented Programming (OOP) basics

https://www.youtube.com/watch?v=ZDa-Z5JzLYM

• Day 13 - 14: Practice exercises and mini-projects

https://www.youtube.com/watch?v=8ext9G7xspg

Week 3-4: Statistics and Probability

• Day 15 - 16: Descriptive statistics – mean, median, mode, variance, standard

deviation
https://www.youtube.com/watch?v=Vfo5le26IhY

• Day 17 - 18: Probability theory basics

https://www.youtube.com/watch?v=Uz3D-c4QzT8

• Day 19 - 20: Probability distributions – normal, binomial, Poisson

https://www.youtube.com/watch?v=5Dnw46eC-0o

• Day 21 - 22: Inferential statistics – hypothesis testing and confidence intervals

https://www.youtube.com/watch?v=0zZYBALbZgg

• Day 23 - 24: Correlation and regression analysis

https://www.youtube.com/watch?v=2AQKmw14mHM

• Day 25 - 26: Bayesian statistics fundamentals

https://www.youtube.com/watch?v=HZGCoVF3YvM

• Day 27 - 28: Practice problems and real-world data analysis exercises

Kaggle Datasets for Practice
Month 2: Data Manipulation and Visualization
Week 5-6: Data Manipulation with Pandas

• Day 29 - 30: Introduction to Pandas – dataframes and series

https://www.youtube.com/watch?v=vmEHCJofslg

• Day 31 - 32: Data cleaning – handling missing data, duplicates

https://www.youtube.com/watch?v=5RaviF3FNuQ

• Day 33 - 34: Data transformation – filtering, merging, grouping

https://www.youtube.com/watch?v=txMdrV1Ut64

• Day 35 - 36: Time series analysis with Pandas

https://www.youtube.com/watch?v=zmfe2RaX-14

• Day 37 - 38: Practice exercises with real datasets

Kaggle Pandas Exercises

Week 7-8: Data Visualization

• Day 39 - 40: Introduction to Matplotlib – creating basic plots

https://www.youtube.com/watch?v=UO98lJQ3QGI

• Day 41 - 42: Customizing plots – labels, legends, styles

https://www.youtube.com/watch?v=Ercd-Ip5PfQ

• Day 43 - 44: Introduction to Seaborn – statistical data visualization

https://www.youtube.com/watch?v=6GUZXDef2U0

• Day 45 - 46: Advanced visualizations – heatmaps, pair plots

https://www.youtube.com/watch?v=0yY2Wha7TcA

• Day 47 - 48: Interactive visualizations with Plotly

https://www.youtube.com/watch?v=GGL6U0k8sHU

• Day 49 - 50: Creating dashboards and storytelling with data

https://hbr.org/2014/04/the-fourth-era-of-marketing
Month 3: Machine Learning Fundamentals

Week 9-10: Supervised Learning

• Day 51 - 52: Introduction to machine learning concepts

Machine Learning Crash Course – Google

• Day 53 - 54: Linear regression – theory and implementation

https://www.youtube.com/watch?v=ZkjP5RJLQF4

• Day 55 - 56: Logistic regression – theory and implementation

https://www.youtube.com/watch?v=yIYKR4sgzI8

• Day 57 - 58: Decision Trees and Random Forests

https://www.youtube.com/watch?v=7VeUPuFGJHk

• Day 59 - 60: Support Vector Machines (SVM)

https://www.youtube.com/watch?v=efR1C6CvhmE

• Day 61 - 62: Model evaluation – Cross-validation, precision, recall, F1-score

https://www.youtube.com/watch?v=85dtiMz9tSo

Week 11: Unsupervised Learning

• Day 63 - 64: Clustering techniques – K-Means, DBSCAN, Hierarchical

https://www.youtube.com/watch?v=4b5d3muPQmA

• Day 65 - 66: Dimensionality reduction – PCA, t-SNE

https://www.youtube.com/watch?v=FgakZw6K1QQ

• Day 67 - 68: Anomaly detection techniques

https://www.youtube.com/watch?v=0oWl_ONfflE

Week 12: Deep Learning & Natural Language Processing (NLP)

• Day 69 - 70: Introduction to Deep Learning and Neural Networks

https://www.youtube.com/watch?v=aircAruvnKk

• Day 71 - 72: Convolutional Neural Networks (CNNs) for image processing

https://www.youtube.com/watch?v=FmpDIaiMIeA

• Day 73 - 74: Recurrent Neural Networks (RNNs) and LSTMs for NLP
https://www.youtube.com/watch?v=WCUNPb-5EYI

• Day 75 - 76: Deploying machine learning models using Flask

https://www.youtube.com/watch?v=tu6L2MiqAAU

• Day 77-78: Web Scraping with BeautifulSoup and Scrapy

https://www.youtube.com/watch?v=XVv6mJpFOb0
Python `
Python is a highly popular language for data science, known for its simplicity,
readability, and extensive library support. It's widely used for data analysis,
visualization, and building machine learning models.

Estimated time: 2 months

Learning resources: Python Full Course for Beginners

Complete Python Mastery

Essential Concepts

▪ Python Fundamentals
▪ Variables and data types
▪ Loops (for, while) and conditional statements (if, elif, else)
▪ Functions and scope

Data Structures
▪ Arrays, lists, tuples and sets
▪ Stacks and queues
▪ Dictionaries
▪ Comprehensions
▪ Generator expressions

Exception Handling
▪ Handling exceptions with try/except
▪ Raising exceptions

Functional Programming
▪ Lambda functions
▪ Map, reduce, filter

Object-oriented Programming
▪ Classes and objects
▪ Inheritance and polymorphism

Modules and packages

▪ Creating modules
▪ Managing packages with pip and pipenv
▪ Virtual environments

Python Standard Library

▪ Working with paths, files, and directories
▪ Working with CSV and JSON files
▪ Working with Date/time
▪ Generating random values

Familiarity with data science libraries

▪ NumPy
▪ Pandas
▪ Matplotlib
Version Control (Git)

Git is a version control system that is crucial for managing code and collaboration in data
science projects. It allows you to track changes, collaborate with others, and maintain the
integrity of your codebase.

Estimated time: 1- 2 weeks

Learning resources: Git Tutorial for Beginners: Learn Git in 1 Hour

The Ultimate Git Course

Essential Concepts

▪ Setup and Configuration: init, clone, config

▪ Staging: status, add, rm, mv, commit, reset
▪ Inspect and Compare: log, diff, show
▪ Branching: branch, checkout, merge
▪ Remote Repositories: remote, fetch, pull, push
▪ Temporary Commits: stash
▪ GitHub: fork, pull request, code review

SQL
SQL (Structured Query Language) is essential for querying and managing data in relational
databases. It's a fundamental skill for any data scientist working with structured data.

Estimated time: 1 - 2 months

Learning resources: SQL Course for Beginners [Full Course]

Complete SQL Mastery

Essential Concepts

Basic Operations
▪ Querying data (SELECT)
▪ Modifying data (INSERT, UPDATE, DELETE)
▪ Filtering data (WHERE, IN, BETWEEN, LIKE, IS NULL, REGEXP)
▪ Logical operators (AND, OR, NOT)
▪ Sorting and limiting data (ORDER BY, LIMIT)

Complex Queries
▪ Joins (INNER, OUTER, SELF, NATURAL, CROSS)
▪ Aggregate functions (MAX, MIN, AVG, SUM, COUNT)
▪ Grouping data (GROUP BY, HAVING, ROLLUP)
▪ Subqueries

Views
Stored Procedures and Functions

Triggers and Events

Transactions
▪ Transaction isolation levels
▪ BEGIN, COMMIT, ROLLBACK

Database Design
▪ Normalization
▪ Database integrity with primary keys, foreign keys, and constraints

Indexes

Security and Permissions: Managing users and privileges

Data Structures & Algorithms

Understanding data structures and algorithms is crucial for optimizing code and solving
complex problems efficiently. This knowledge is fundamental for technical interviews and
real-world data science tasks.

Estimated Time: 1 - 2 months

Learning resources: Data Structures and Algorithms for Beginners

The Ultimate Data Structures & Algorithms Bundle

Essential Concepts

Big O Notation

Arrays and Linked Lists

Stacks and Queues

Hash Tables

Trees and Graphs

▪ Binary trees
▪ AVL trees
▪ Heaps
▪ Tries
▪ Graphs

Sorting Algorithms
▪ Bubble sort
▪ Selection sort
▪ Insertion sort
▪ Merge sort
▪ Quick sort
▪ Counting sort
▪ Bucket sort
Searching algorithms
▪ Linear search
▪ Binary search
▪ Ternary search
▪ Jump search
▪ Exponential search

String Manipulation Algorithms

▪ Reversing a string
▪ Reversing words
▪ Rotations
▪ Removing duplicates
▪ Most repeated character
▪ Anagrams
▪ Palindrome

Recursion

Mathematics and Statistics

Mathematics and statistics are fundamental for understanding data science concepts. They
provide the theoretical foundation for data analysis and machine learning algorithms.

Estimated Time: 2 - 3 months

Essential Concepts

Linear Algebra
▪ Vectors and matrices
▪ Matrix operations
▪ Eigenvalues and eigenvectors
▪ Singular Value Decomposition (SVD)

Calculus
▪ Derivatives and gradients
▪ Partial derivatives
▪ Chain rule
▪ Integrals

Probability
▪ Probability distributions
▪ Bayes' theorem
▪ Random variables
▪ Expectation and variance

Statistics
Data Collection and Visualization

Effective data handling, processing, and visualization are critical for preparing data for
analysis and communicating results. This involves cleaning, transforming, exploring, and
visualizing data.

Estimated Time: 1 - 2 months

Essential Concepts

Data Cleaning
▪ Handling missing values
▪ Removing duplicates
▪ Outlier detection and treatment

Data Transformation
▪ Normalization and standardization
▪ Encoding categorical variables
▪ Feature scaling

Exploratory Data Analysis (EDA)

▪ Summary statistics
▪ Data visualization (using libraries like Matplotlib, Seaborn)
▪ Identifying patterns and correlations

Data Integration
▪ Merging and joining datasets
▪ Data aggregation
▪ Handling different data formats (CSV, JSON, SQL)

Machine Learning Fundamentals

Understanding machine learning fundamentals is crucial for building predictive models. This
involves learning about different algorithms and how to train and evaluate models.

Estimated Time: 2 - 3 months

Essential Concepts

Supervised Learning
▪ Regression algorithms (e.g., linear regression, logistic regression)
▪ Classification algorithms (e.g., decision trees, k-nearest neighbors, support vector
machines)

Unsupervised Learning
▪ Clustering algorithms (e.g., K-means, hierarchical clustering)
▪ Dimensionality reduction techniques (e.g., PCA, LDA)

Model Evaluation
▪ Accuracy
▪ Precision-Recall
▪ F1 score
▪ ROC - AUC
 ▪ Confusion matrix

Model Training
▪ Train-test split
▪ Cross-validation
▪ Hyperparameter tuning

Overfitting and Underfitting

▪ Recognizing overfitting and underfitting
▪ Techniques to mitigate overfitting (e.g., regularization, dropout)
▪ Model complexity management

Deep Learning

Deep learning is a subset of machine learning that involves neural networks with many
layers. These models are powerful for handling large-scale data and complex patterns.

Estimated Time: 2 - 3 months

Essential Concepts

Neural Networks
▪ Basics of neural networks
▪ Activation functions
▪ Forward and backward propagation

Advanced Neural Networks

▪ Convolutional Neural Networks (CNNs)
▪ Recurrent Neural Networks (RNNs)

Deep Learning Frameworks

▪ Tools: TensorFlow, PyTorch, Keras

Specialization

Specializing in a specific area of data science allows you to develop expertise and stand out
in the field. Two popular tracks are Natural Language Processing (NLP) and Computer
Vision.

Estimated Time: 2 - 3 months

Essential Concepts

Natural Language Processing (NLP)

▪ Text preprocessing (tokenization, stemming, lemmatization)
▪ Sentiment analysis
▪ Named entity recognition (NER)
▪ Language modeling (using libraries like NLTK, SpaCy, Hugging Face)

Computer Vision
▪ Image Classification: Techniques and models
▪ Object Detection: Algorithms like YOLO, SSD
 ▪ Image Segmentation: Semantic and instance segmentation
▪ Generative Models: GANs in computer vision

Big Data (Optional)

Big data skills are valuable for processing and analyzing large datasets, which is essential
for certain data science roles. Understanding big data technologies can enhance your
capabilities and make you more competitive in the job market.

Estimated Time: 2 - 3 months

Essential Concepts

▪ Big Data Frameworks: Hadoop, Spark

▪ Data Processing: MapReduce, Spark SQL

▪ Data Storage: HDFS, NoSQL databases (Cassandra, MongoDB)

▪ Data Ingestion: Kafka, Flume

 2. COURSEWORK

1. Linear Algebra
• Definition: The study of vectors, matrices, and linear transformations, forming the
foundation for ML algorithms.

• Importance: Essential for understanding PCA, SVD, and deep learning models.

• Key Concepts: Vectors, Matrices, Eigenvalues, Eigenvectors, Singular Value

Decomposition (SVD).

• Resources:
Essence of Linear Algebra – 3Blue1Brown (YouTube)
Linear Algebra for Machine Learning – Coursera

2. Probability & Statistics

• Definition: The mathematical study of data uncertainty, critical for inferencing in ML
models.

• Importance: Used in hypothesis testing, regression, Bayesian models, and A/B

testing.

• Key Concepts: Probability Distributions, Bayes’ Theorem, Central Limit Theorem,

Variance, Standard Deviation.

• Resources:
Statistics for Data Science – Khan Academy
Data Science Probability & Stats – HarvardX (edX)

3. Calculus

• Definition: The mathematical study of continuous change, crucial for optimization in

ML.

• Importance: Required for gradient descent, cost function optimization in ML/DL.

• Key Concepts: Differentiation, Partial Derivatives, Chain Rule, Integrals, Gradient

Descent.

• Resources:
Calculus for Machine Learning – StatQuest (YouTube)
MIT Calculus Course – OCW

4. Hypothesis Testing
• Definition: A statistical method for making inferences about data populations.

• Importance: Used to validate ML models and business decisions.

 • Key Concepts: Null Hypothesis, Alternative Hypothesis, p-value, Confidence
Intervals.

• Resources:
Hypothesis Testing – Khan Academy
Applied Statistics for Data Science – Coursera

5. A/B Testing

• Definition: A controlled experiment technique used to compare two versions of a

product or model.

• Importance: Used in marketing, UI/UX, and performance evaluation of ML models.

• Key Concepts: Randomized Control Trials, Statistical Significance, Conversion

Rates, p-values.

• Resources:
A/B Testing Explained – Udacity
DataCamp A/B Testing Course

6. Optimization Techniques
• Definition: Methods to improve machine learning models' efficiency and
performance.

• Importance: Required for training deep learning models efficiently.

• Key Concepts: Gradient Descent, Stochastic Gradient Descent (SGD), Adam,

RMSprop.

• Resources:
Optimization for ML – Coursera
Gradient Descent Explained – StatQuest
 3. OTHER SKILLS

1. Case Studies
• Definition: Real-world applications of data science in various industries.

• Importance: Helps in understanding how theoretical concepts apply in practice.

• How to Learn:
- Read research papers on AI/ML applications.
- Analyze Kaggle competitions and case studies from top companies.

• Resources:
Google Cloud AI Case Studies
Kaggle Real-World Data Science Case Studies

2. Behavioural Q&A (Interview Skills)

• Definition: Non-technical interview questions that assess problem-solving and

teamwork skills.

To answer these questions, you have to follow the STAR method:

- Situation: Describe the context or background of the scenario.

- Task: Explain your role and the challenge you faced.
- Action: Detail the steps you took to address the task.
- Result: Highlight the outcomes or impact of your actions

• Importance: 80% of hiring decisions are influenced by behavioural answers.

• Common Questions:
- Tell me about yourself?
- Describe a challenging project and how you handled it.

• Resources:
Cracking Data Science Interviews – Interview Query
Mock Interviews – Pramp

3. Business Storytelling
• Definition: Presenting data-driven insights in a compelling way.

• Importance: Essential for communicating results to non-technical stakeholders.

• How to Learn:
- Practice creating story-driven reports using Power BI/Tableau.
- Follow frameworks like McKinsey’s Pyramid Principle.
• Resources:
Data Storytelling for Business – Udemy
The Pyramid Principle – Barbara Minto
4. Communication & Presentation
• Definition: The ability to present findings effectively using visualizations.

• Importance: 60% of a data scientist’s job involves explaining results.

• How to Learn:
- Practice with PowerPoint, Tableau, and Jupyter Notebook.
- Learn how to create executive-level reports.

• Resources:
Effective Data Science Communication – Coursera
Public Speaking for Data Scientists – Toastmasters
 4. CERTIFICATIONS

Certificates are important for data scientist job interviews because:

1. Validation of Skills: Certificates prove your proficiency in specific tools and

techniques.

2. Credibility: They enhance your resume by showing formal training and meeting
industry standards.

3. Competitive Edge: They help you stand out in a crowded job market.

4. Benchmarking: Certificates align your skills with industry expectations.

5. Confidence Boost: They ensure your abilities and knowledge during interviews.

These are the few which you can do to enhance your skills.

Certified Analytics Professional (CAP)

• Website: Certified Analytics
• Link: https://www.certifiedanalytics.org/certification/cap

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

• Website: DASCA
• Link: https://www.dasca.org/certifications/senior-data-scientist

Data Science Council of America (DASCA) Principal Data Scientist (PDS)

• Website: DASCA
• Link: https://www.dasca.org/certifications/principal-data-scientist

Open Certified Data Scientist (Open CDS)

• Website: The Open Group
• Link: https://www.opengroup.org/certifications/open-certified-data-scientist

SAS Certified Big Data Professional

• Website: SAS
• Link: https://www.sas.com/en_us/certification/credentials/data-scientist/big-data-
professional.html

Microsoft Certified: Azure Data Scientist Associate

• Website: Microsoft Learn
• Link: https://learn.microsoft.com/en-us/certifications/azure-data-scientist/

IBM Data Science Professional Certificate

• Website: Coursera
• Link: https://www.coursera.org/professional-certificates/ibm-data-science

Google Data Analytics Professional Certificate

• Website: Coursera
• Link: https://www.coursera.org/professional-certificates/google-data-analytics

Coursera Data Science Courses

• Website: Coursera
• Link: https://www.coursera.org/browse/data-science
Great Learning Academy Free Data Science Courses
• Website: Great Learning Academy
• Link: https://www.mygreatlearning.com/academy/learn-for-free/courses/data-science

IBM SkillsBuild
• Website: IBM SkillsBuild
• Link: https://skillsbuild.org/

Certifications based on individual skills:

Machine Learning Certifications

1. AWS Certified Machine Learning – Specialty

AWS Certification

2. Google Cloud Professional Machine Learning Engineer

Google Cloud Certification

3. IBM Machine Learning Professional Certificate

Coursera

4. Microsoft Certified: Azure AI Engineer Associate

Microsoft Learn

Programming Certifications (Python, R, SQL)

1. Python for Data Science and Machine Learning Bootcamp

Udemy

2. SQL for Data Science

Coursera

3. IBM Data Science Professional Certificate

Coursera

Data Visualization Certifications

1. Microsoft Certified: Power BI Data Analyst Associate

Microsoft Learn

2. Tableau Desktop Specialist Certification

Tableau

3. Data Visualization with Python (Matplotlib, Seaborn, Plotly)

Udemy
 Data Analysis Certifications

1. Google Data Analytics Professional Certificate

Coursera

2. Data Analyst Nanodegree

Udacity

3. Data Wrangling with Python

DataCamp

Mathematics for Data Science Certifications

1. Mathematics for Machine Learning

Coursera

2. Statistics and Probability for Data Science

HarvardX - edX

IDE and Notebook Certifications

1. Jupyter Notebook and Python for Data Science

Udemy

2. Data Science Tools (Jupyter, Google Colab, Kaggle Notebooks, etc.)

Coursera

Cloud Deployment Certifications (AWS, Azure)

1. AWS Certified Solutions Architect – Associate

AWS Certification

2. Microsoft Certified: Azure Data Engineer Associate

Microsoft Learn

Web Scraping Certifications

1. Web Scraping with Python and BeautifulSoup

Udemy

2. Scrapy: Web Scraping with Python

Udemy
PHASE 2 : JOB
SEARCH
 1. HANDS-ON PROJECT

Projects are important because they demonstrate your practical skills and ability to apply
knowledge to real-world problems. They build a strong portfolio, showcase your problem-
solving abilities, and help differentiate you from other candidates. Additionally, projects
support your professional growth by exposing you to diverse tasks and industry trends. If
you're currently employed, you can showcase the existing projects of your company, whether
they're related to reporting, ad-hoc analysis, or other tasks.

What to do?

• Select a real-world dataset from Kaggle, UCI Machine Learning Repository, or

Data.gov.
• Choose a project type: Predictive modeling, NLP, Time series analysis, or
Business analytics.
• Use Python (Pandas, NumPy, Scikit-learn) or R to clean, analyze, and build
models.
• Document everything in a Jupyter Notebook and upload it to GitHub or Kaggle.
• Create a portfolio website (using Notion, Medium, or GitHub Pages) to display your
work.

Resources:

• Kaggle (Datasets & Competitions) – https://www.kaggle.com/

• GitHub for Data Science – https://github.com/topics/data-science
• YouTube: Ken Jee’s Portfolio Building Guide –
https://www.youtube.com/@KenJee

Real-time Projects:

Beginner Level

1. Exploratory Data Analysis (EDA) on a Public Dataset

Dataset: Titanic, Netflix Shows
Example Notebook: Titanic EDA

2. Customer Segmentation using K-Means Clustering

Dataset: Mall Customers
Example Notebook: Customer Segmentation

3. Sentiment Analysis on Product Reviews (NLP)

Dataset: Amazon Reviews
Example Notebook: Sentiment Analysis

Intermediate Level

4. Stock Price Prediction Using LSTM (Time Series Analysis)

Dataset: Yahoo Finance API
Example Notebook: Stock Prediction

5. Fraud Detection in Credit Card Transactions

Dataset: Credit Card Fraud
Example Notebook: Fraud Detection
 6. Movie Recommendation System (Collaborative Filtering)
Dataset: MovieLens Dataset
Example Notebook: Movie Recommender

Advanced Level

7. End-to-End Chatbot using Transformers (NLP + API)

Dataset: Cornell Movie Dialogs
Example Notebook: Chatbot with Transformers

8. Predicting Customer Churn for a Telecom Company

Dataset: Telco Churn Data
Example Notebook: Customer Churn

9. Traffic Sign Recognition using CNNs (Computer Vision)

Dataset: German Traffic Sign Dataset
Example Notebook: Traffic Sign Recognition

10. Real-time Object Detection Using YOLOv8 (Deep Learning + Edge AI)
Dataset: COCO Dataset
Example Notebook: YOLO Object Detection
 2. RESUME BUILDING

Your resume should be concise, ATS-friendly, and highlight impact.

What to do?

• Use a one-page format (unless you have 10+ years of experience).

• Highlight technical skills, tools, and relevant projects.
• Quantify achievements: "Reduced processing time by 30%" instead of "Worked
on optimization."
• Include links to GitHub, portfolio, or Kaggle profiles.
• Use tools like Canva, NovoResume, or Overleaf (LaTeX) for formatting.

Resources:

• Best Data Science Resume Templates – https://resumeworded.com/

• YouTube: Resume Review by Krish Naik – https://www.youtube.com/@KrishNaik
• LinkedIn Resume Writing Guide – https://www.linkedin.com/pulse/how-write-data-
science-resume/

Here are the top 10 resume-building tips:

1. Tailor Your Resume: Customize your resume for each job application by highlighting
relevant skills and experiences specific to the job description.

2. Use Action Verbs: Start bullet points with strong action verbs like "achieved,"
"developed," or "led" to convey your accomplishments.

3. Quantify Achievements: Include specific metrics, numbers, or percentages to

demonstrate the impact of your work.

4. Keep It Concise: Aim for a clear and concise format, ideally one page for early-
career professionals and up to two pages for those with more experience.

5. Highlight Key Skills: Emphasize both technical and soft skills that are crucial for the
role you're applying for.

6. Include Keywords: Use keywords from the job description to pass Applicant
Tracking Systems (ATS) and capture the recruiter’s attention.

7. Professional Formatting: Use a clean, professional layout with consistent fonts,

bullet points, and spacing for easy readability.

8. Showcase Relevant Experience: Focus on your most relevant job experiences,

projects, and accomplishments that align with the job you're applying for.

9. Include a Summary Statement: Start with a summary or objective statement that

highlights your career goals and key strengths.

10. Proofread Carefully: Ensure there are no typos, grammatical errors, or

inconsistencies by proofreading your resume thoroughly or having someone else
review it.
 3. LINKEDIN/NAUKRI OPTISATION
Recruiters use LinkedIn & Naukri to find candidates. Optimizing your profile increases
visibility.

1. Profile Summary

● LinkedIn: Craft a compelling headline that clearly states your role, skills, and key
achievements. Use keywords related to Data Science to increase visibility in search results.

● Naukri/Job Portals: Write a concise and impactful summary that highlights your
experience, skills, and career aspirations. Make sure to include keywords relevant to Data
Scientist roles.

2. Experience Section

● Detail Your Roles: For each position, provide a clear and concise description of your
responsibilities, achievements, and the impact you had. Use bullet points for better
readability.

● Quantify Achievements: Include specific metrics and examples (e.g., “Increased sales
forecasting accuracy by 20% through advanced statistical analysis”).

3. Skills and Endorsements

● Highlight Key Skills: List relevant skills such as SQL, Python, ML, AI, Deep Learning,
NLP, data visualization, statistical analysis, and business intelligence tools. Ensure that
these skills are aligned with the job descriptions of the roles, you are targeting.

● Get Endorsements: Seek endorsements from colleagues, mentors, or managers who

can vouch for your expertise in these areas.

4. Certifications and Education

● Showcase Certifications: Add any relevant certifications (e.g., Certified Data Scientist,
Python/ML Certification) to your profile. Ensure they are visible and up-to-date.

● Update Education: List your educational qualifications, including any relevant

coursework or projects that pertain to Data Scientist.

5. Projects and Achievements

● Include Notable Projects: Highlight significant projects you’ve worked on. Provide a brief
description, of your role, and the outcomes achieved.

● Showcase Awards and Recognition: Add any awards or recognitions you’ve received
for your work in Data Science.

6. Recommendations

● Request Recommendations: Ask for recommendations from supervisors, colleagues, or

clients who can provide testimonials about your work ethic, skills, and contributions.
7. Profile Picture and Banner

● Professional Picture: Use a high-quality, professional profile picture. A friendly,

approachable image can make a positive impression.

● Custom Banner: Consider adding a custom banner that reflects your professional brand
or highlights your expertise in Data Science.

8. Keywords and SEO

● Incorporate Keywords: Use industry-specific keywords throughout your profile to

improve your visibility in search results. Tailor these keywords to the roles you are targeting.

● Optimize for Search: Regularly update your profile and ensure it reflects the latest
industry trends and skills.

9. Networking and Engagement

● Connect with Industry Professionals: Expand your network by connecting with other
Data Scientist, recruiters, and industry leaders.

● Engage with Content: Share relevant articles, write posts, and engage with content
related to Data Science to increase your visibility and showcase your expertise.

Resources:

• LinkedIn Profile Optimization Guide – https://www.linkedin.com/pulse/linkedin-

profile-optimization/
• Naukri Job Search Strategy – https://www.naukri.com/blog/how-to-make-your-
resume-visible-to-recruiters/
• YouTube: Optimizing LinkedIn for Data Science –
https://www.youtube.com/@AlexTheAnalyst
 4. APPLY FOR JOBS
After profile optimization, start applying strategically rather than mass-applying.

What to do?

• Apply for roles that match 70%+ of your skillset.

• Customize your resume & cover letter for each job (mention specific skills from the
job description).
• Use job platforms:
General: LinkedIn, Indeed, Glassdoor, Naukri, Instahyre
Tech-Specific: Kaggle Jobs, DataJobs, AI-Jobs.net
• Apply via referrals (reach out to employees via LinkedIn with a short, personalized
message).
• Track applications using Notion, Trello, or an Excel sheet.

However, remember that NOT ALL companies, like Zomato etc, are listed on job
portals—some rely solely on referrals. So, connect with people on LinkedIn or reach out
to friends to secure referrals.

Use Below Message for Seeking Referral :

Hi [Name],

I hope this message finds you well. I came across an opening for [Position Name] at
[Company Name] and am very interested in applying. With my background in [briefly
mention your skills/experience relevant to the job], I believe I would be a great fit for this role.

I noticed that you are connected to [Company Name], and I would greatly appreciate it if you
could refer me for this position. I have attached my resume for your reference and would be
happy to provide any additional information needed.

Thank you for considering my request. I look forward to the possibility of connecting further.

Best regards,
[Your Name]

Resources:

• Best Job Boards for Data Science – https://datasciencereport.com/best-job-

boards/
• YouTube: How to Get Data Science Job Without Experience –
https://www.youtube.com/@KrishNaik
 5. INTERVIEW

Interview preparation is a crucial step in your journey to securing a data science role. It
involves a structured approach to understanding the job requirements, refining your skills,
and practicing to present yourself confidently during interviews. Here's an in-depth guide to
help you prepare effectively:

1. Understand the Job Description

▪ Analyze Key Skills: Review the job description thoroughly to identify required skills
such as Python, SQL, machine learning, deep learning, data visualization, and cloud
computing.

▪ Identify Core Responsibilities: Understand the main responsibilities, including data

preprocessing, model development, evaluation, and deployment.

▪ Research the Company: Learn about the company's business model, industry
trends, competitors, and how data science contributes to their objectives.

2. Strengthen Your Technical Skills

▪ SQL Mastery: Practice complex queries, joins, window functions, and performance
optimization using platforms like LeetCode and HackerRank.

▪ Python Proficiency: Focus on data manipulation (Pandas, NumPy), machine

learning (Scikit-learn, TensorFlow, PyTorch), and data visualization (Matplotlib,
Seaborn).

▪ Statistics & Probability: Understand key concepts such as hypothesis testing,

regression analysis, confidence intervals, and Bayesian inference.

▪ Machine Learning & AI: Strengthen your grasp of supervised, unsupervised, and
deep learning techniques. Practice implementing models from scratch and using
libraries like Scikit-learn and TensorFlow.

▪ Big Data & Cloud Technologies: Familiarize yourself with tools such as Hadoop,
Spark, AWS, and GCP for large-scale data processing.

3. Behavioural Interview Preparation

▪ Use the STAR Method: Structure responses using Situation, Task, Action, and
Result to showcase problem-solving, collaboration, and critical thinking.

▪ Common Questions:
"Tell me about a time you used data science to solve a complex problem."
"Describe a challenging project and how you approached it."
"How do you handle tight deadlines or conflicting priorities?"

▪ Prepare Impactful Stories: Highlight experiences that demonstrate analytical

thinking, leadership, and technical expertise.
4. Mock Interviews & Hands-on Practice

▪ Simulate Interviews: Schedule mock interviews with mentors or use platforms like
Pramp and Interviewing.io.

▪ Feedback & Improvement: Act on feedback to strengthen weak areas, particularly

in technical explanations and coding challenges.

5. Prepare Questions for the Interviewer

• Ask Insightful Questions: Show your curiosity and understanding of the role by
asking about:
- The key business problems the data science team is tackling.
- Collaboration between data science and other departments.
- Challenges the company faces in deploying data science solutions.

6. Showcase Your Projects & Achievements

▪ Discuss Relevant Projects: Be ready to present your previous work, emphasizing

the objective, approach, tools used, and impact.

▪ Build a Portfolio: Maintain a portfolio (e.g., on GitHub or a personal website)

showcasing your data science projects with well-documented code and
visualizations.

7. Technical Test & Coding Challenges

▪ Prepare for Assessments: Many companies test SQL, Python, and machine
learning knowledge through coding exercises and case studies.

▪ Practice Time Management: Solve problems under time constraints to simulate real
test conditions.

8. Confidence & Presentation Skills

▪ Explain Your Thought Process Clearly: Articulate your reasoning when solving
problems or discussing models.

▪ Maintain Good Body Language: Show confidence with good posture, eye contact,
and a positive tone.

▪ Practice Technical Presentations: If required to present, rehearse multiple times to

ensure a smooth and professional delivery.

Resources:

• Top 50 Data Science Interview Questions – https://towardsdatascience.com/top-

data-science-interview-questions/
• YouTube: Mock Data Science Interviews –
https://www.youtube.com/@DataScienceDreamJob
 • LeetCode Data Science Questions – https://leetcode.com/discuss/interview-
question?currentPage=1&orderBy=hot&query=data%20science

-------------------------------------------------------------------------------------------------------------------------

Mazher Khan - IIT (BHU) - B.Tech (DR-2)

Senior Data Analyst @TARGET | Ex - OLX (EU)
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