Data Science Roadmap — 2024
Learn Effectively :)
In this blog I will provide the complete roadmap of Data Science.
Most people advise that statistics, mathematics should be learned first which is definitely true but I felt that while studying those, you will not be able to find the sole purpose. You may feel stuck. So My preference is to start things parallelly. That will not make you feel bored and will give you a broader prospect.
“Being realistic is preferable to being perfectionist”
Let’s start:
Programming:
Python and R both are widely used for this purpose. However, Python is the most simple and easy language. I would recommend you to learn Python.
The Reason behind the dominance of Python in Data Science field.
Basics of Python: Variables, Data Types, Type Casting, Operators, If-Else Conditional Statements, Loops, Strings, Functions, List, Tuple, Set, Dictionary, Zip, Enumerate, Map, Filter, Reduce, OOPs Concept (Encapsulation, Abstraction, Polymorphism, Inheritance), Decorators.
Complete explanations of all the topics in my You Tube channel.
Python Libraries : Numpy, Pandas, Matplotlib, Seaborn, Plotly
Completely Free Tutorial on Python Libraries in my YouTube channel.
Statistics:
Types of Data, Sampling, Definition of Statistics and its types,Descriptive Statistics, Inferential Statistics
Mathematics:
Linear Algebra, Probability (Probability Fundamentals and Conditional Probability, Bayesian Probability, Probability Distributions, Central limit theorem), Z-Scores, Scipy States
Machine Learning Steps:
Introduction: Definition of Machine Learning( Supervised and Unsupervised Learning), Reinforcement Learning, Artificial Intelligence, ML, DL, NLP Definitions, Job Roles of Data Professionals, Lifecycle of Data Science Project.
Ongoing Playlist on Machine Learning..
Exploratory Data Analysis (6 processes): Discovering , Structuring, Cleaning (Handle Missing Values, Removing Outliers (using IQR, Using Z-Score), Log Transformations to mitigate the effect of Outliers), Joining, Validating, Presenting.
Feature Engineering: Feature selection, Feature Transformation, Feature Extraction.
Complete EDA and Feature Engineering Tutorial in my You tube Channel.
Models : Linear Regression, Logistic Regression, SVM, K-means, Naive Bayes, Decision Tree, Ensemble Techniques (Bagging, Boosting), Random Forest, XGBoost, AdaBoost
Other important topics: Definition of Hyper Parameter, How to find the best Hyper Parameter
Deep Learning:
Introduction: Relationship among Artificial Intelligence- Machine Learning - Deep Learning. An Introduction to Deep Learning (Timeline of Deep Learnings' Ascent, Key Personalities in this field, Difference between Machine Learning and Deep Learning, Reason behind the increasing popularity of Deep Learning, Types of Neural Network), Perceptron vs. Neuron, Perceptron Loss Function, Problem with Perceptron.
ANN (Artificial Neural Network): Multilayer Perceptron, Forward Propagations, Backpropagation, Loss Functions, Gradient Descent and problems (Batch Gradient Descent, Stochastic Gradient Descent, Mini Batch Gradient Descent), How to improve performance of Neural Networks (Vanishing Gradient Problem (Activation Functions, Weight Initialization), Overfitting (Dropout Layers, Regularization, Early Stopping), Normalization, Optimizers).
Common Concepts: Regularization techniques used in Deep Learning, Type of Activation Functions in Deep Learning.
CNN (Convolutional Neural Network): Convolution Basics, Kernel (Filter) Operations, Padding and Strides Operation, Pooling Layer in CNN, Data Augmentation, Pre-trained Models, Transfer Learning (LeNet, AlexNet, VGG, etc. ), Interview Questions.
RNN (Recurrent Neural Network): Basics of RNN, Forward Propagation and Backward Propagation, Problems with RNN, Long Short-Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU), Stacked RNN, Bidirectional RNNs,
Sequence-to-Sequence Models: Encoder Decoder, Attention Mechanisms, Transformer Architecture, Transfer Learning, LLMs.
NLP (Natural Language Processing):
Practical applications of NLP, NLP Pipeline ( Data Acquisition, Text Preprocessing (Tokenization, Stemming, Lemmatization, One Hot Encoding, Bag of Words, Term Frequency-Inverse Document Frequency, Word2Vec) , Text Representation, NLP Tasks, Machine Learning Models, Post Processing)
Generative AI:
Introduction to Generative AI, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive Models (RNNs, Transformers), Flow-based Models (Normalizing Flows), Applications in Image, Text, Audio, and Video Generation, LLM.
Structured Query Language:
Relational Databases and SQL, Basic Data Retrieval, Calculations, Using Functions, Sorting Data, Column /Row based logic, Boolean logic, Inexact Matches, Join, Subqueries, Window functions.
Cloud Computing:
Definition of Cloud Computing, Cloud Service Models, Cloud Deployment Models.
Projects:
Projects are essential for applying knowledge and skills in real-world scenarios making you a more compelling candidate in interviews.
I will share the Github link of my projects as I believe they will be beneficial for your insight and understanding.
- Explore a project delving into penguin insights through the application of the K Means model.
Resume and Interview Preparation:
Resume: You have numerous options available for creating your resume at no cost, and I highly recommend Canva as an excellent platform for this purpose.
Be very cautious while creating your Resume and hobbies are important.
Interview Preparation: I’ve curated a comprehensive interview preparation playlist covering Python programming questions for beginners to pros. It’s a complete interview series on Python, designed to help you excel in your interviews.
Portfolio Website: A portfolio website is essential for professionals to showcase their work, build their brand, and attract potential clients or employers enhancing credibility and accessibility.
Make your Portfolio website look beautiful.
Feel free to explore my portfolio website for inspiration on building your own and elaborate yourself tailored to your unique skills and expertise.
Here, I have provided the topics you need to be prepared for becoming a professional in Data Science field. I will write about the articles I have mentioned above and will attach the links accordingly. Hence, Keep visiting for more updates.
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Other Blogs related to Data Science World:
Artificial Intelligence-people are confused but a simple tale- Introduction to AI
Understanding the Distinctions: Machine Learning, Deep Learning, and Natural Language Processing.
Understand the key difference of Artificial Intelligence and Data Science in a form of a story
How different Data Professionals contribute in a single project..
Data Scientist vs Data Engineer.
Aspiring to be a Data Professional? But don’t know how to start.
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