This is ChatGPT Book Data Science Roadmap.pdf

THE COMPLETE
DATA SCIENCE ROADMAP
Go From Zero to a Data Scientist
in 12 Months
Mosh Hamedani

2
Copyright 2024 Code with Mosh codewithmosh.com
Hi! I am Mosh Hamedani, a software engineer with over 20
years of experience.
Over the past 10 years, I
’
ve had the privilege of teaching
millions of people how to code and become professional
software engineers through my YouTube channel and online
courses.
It
’
s my mission to make software engineering accessible to
everyone. Join me on this journey and unlock your potential in
the world of coding!
https://codewithmosh.com

Data Science Roadmap 3
Table of Content
Introduction 4
Target Audience 4
Resources 4
Roadmap Overview 5
Python 6
Version Control (Git) 8
Data Structures & Algorithms 9
SQL 11
Mathematics and Statistics 13
Data Collection and Visualization 15
Machine Learning Fundamentals 16
Deep Learning 18
Specialization 19
Big Data
(
Optional) 20

Introduction
This guide is designed to help you navigate the essential skills needed to become
a successful data scientist. Whether you're just starting out or looking to enhance
your existing skills, this roadmap will provide a clear and structured path.
Target Audience
This guide is for:
• Beginners who want to know what they need to learn to land a data science
job.
• Experienced individuals looking to level up their skills and fill in the gaps in
their knowledge.
Resources
For detailed tutorials and full courses, check out the following resources:
• YouTube Channel: https://www.youtube.com/c/programmingwithmosh
• Full Courses: https://codewithmosh.com

Roadmap Overview
Below is a comprehensive table listing all the essential skills needed to become a
proficient data scientist, along with the estimated time required to learn each skill.
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.
Use this roadmap to guide your learning journey and track your progress as you
build a strong foundation in data science.
Skill Est. Time Learning Phase
Programming
(
Python) 1
-
2 months Beginner
Version Control (Git) 1
-
2 weeks Beginner
Data Structures & Algorithms 1
-
2 months Beginner
SQL 1
-
2 months Beginner
Mathematics and Statistics 2
-
3 months Beginner
Data Collection and Visualization 1
-
2 months Intermediate
Machine Learning Fundamentals 2
-
3 months Intermediate
Deep Learning 2
-
3 months Advanced
Specialization
(
NLP or Computer Vision) 2
-
3 months Advanced
Big Data
(
Optional) 2
-
3 months Advanced
Total 12
-
20 months

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: YouTube Tutorial | Full Course
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
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

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
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

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
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

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
• Descriptive statistics (mean, median, mode, standard deviation)
• Hypothesis testing
• Confidence intervals
• Regression analysis

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

Learning to code is a journey. Be patient with yourself and
stay persistent, even when things get tough.
- Mosh

This is ChatGPT Book Data Science Roadmap.pdf

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