This article is an ultimate guide, crafted by the GATE experts at GFG, to help you start your journey of learning for GATE (Graduate Aptitude Test in Engineering) Data Science and AI in 100 Days in a systematic manner.
There are many overlaps when it comes to data science and artificial intelligence (AI). AI has many smaller subsets, like machine learning and deep learning. Data science uses these technologies to interpret and analyze data and find trends and patterns to make predictions. So, the choice between AI vs data science can be tricky.
Machine Learning (ML) depends on strong data science practices to get relevant data in training the ML algorithms and systems. Data science is a field that requires the knowledge of both Artificial Intelligence (AI) and Machine Learning (ML), and many AI careers, like an AI engineer, need the skills of a data scientist.
The demand for data science and AI skills is only increasing by the day. if you preparing for GATE 2024 or looking for a last 3-month preparation strategy?, this is the best place to get a roadmap of your Data Science and AI learning journey along with all the latest job-oriented technologies, designed according to the latest GATE Syllabus.
100 Days of GATE Data Science & AI – A Complete Guide For Beginners
1. Aptitude (Day 1–Day 5)
- Verbal Aptitude
- Grammar: Tenses, articles, adjectives, prepositions, conjunctions, verb-noun agreement, and other parts of speech.
- Vocabulary: words, idioms, and phrases in context, reading and comprehension, Narrative sequencing.
- Quantitative Aptitude
- Data interpretation:
- data graphs (bar graphs, pie charts, and other graphs representing data)
- 2- and 3-dimensional plots, maps, and tables
- Numerical computation and estimation:
- ratios, percentages
- powers, exponents, and logarithms
- permutations and combinations, and series
- Mensuration and Geometry
- Elementary statistics and probability.
- Data interpretation:
- Analytical Aptitude
- Deduction and Induction
- Analogy
- Numerical relations
- Reasoning
- Spatial Aptitude
- Transformation of shapes: translation, rotation, scaling, mirroring, assembling, and grouping Paper folding, cutting, and patterns in 2 and 3 dimensions
2. Statistics and Probability (Day 6–Day 15)
- Counting (permutation and combinations)
- Probability Axioms
- Sample space
- Events
- Independent Events
- Mutually Exclusive events
- Marginal, conditional probability, and joint probability
- Bayes Theorem
- Conditional expectation and variance
- Mean, median, mode, and standard deviation
- Correlation and Covariance
- Random variables
- Discrete random variables
- Probability mass functions
- Uniform distribution
- Bernoulli and binomial distribution
- Continuous Random variables
- Probability Distribution functions
- Different distribution functions(Uniform, Exponential, Poisson, Normal, standard normal)
- Cumulative distribution functions
- Conditional PDF
- Central Limit Theorem
- Confidence Interval
- Z-test
- T-test
- Chi-squared test
3. Linear Algebra (Day 16-Day 25)
- Vector space, subspaces
- Linear Dependence and independence of vectors
- Matrices
- Different types of matrices(Project, Orthogonal, Idempotent, Partition)
- Quadratic Forms
- Systems of Linear Equations
- Gaussian Elimination
- Eigenvalues and Eigenvectors
- Determinant
- Rank
- Nullity
- Projections
- LU decomposition
- Singular value decomposition
4. Calculus and Optimization(Day 26-Day 35)
- Functions of a single Variable
- Limit
- Continuity
- Differentiability
- Taylor Series
- Maxima
- Minima
- Optimization with a Single Variable
5. Programming, Data Structures and Algorithms(Day 36-Day 55)
- Python programming Basic
- Basic Data Structures:
- Search Algorithms:
- Sorting Algorithms:
- Divide and Conquer Algorithms:
- Basic Graph Algorithms
- Traversals and Shortest path
6. Database Warehousing and Management (Day 56-Day 65)
- Relational Databases
- ER Model
- Relational Algebra and Relational Model
- SQL
- Integrity Constraints
- Normal Forms
- File organization
- Indexing
- Data Types
- Data Transformation:
- Normalization
- Discretization
- Sampling
- Compression
- Data Warehouse Modelling
- Multidimensional data models
- Categorization and computations
7. Machine Learning(Day 66-Day 80)
- Supervised Learning
- Regression and Classification problems
- Simple linear Regression
- Multiple Linear Regression
- Ridge Regression
- Logistic Regression
- K-nearest neighbor
- Naive Bayes classifier
- Linear Discriminant Analysis
- Support vector Machine
- Bias-variance trade-off
- Cross Validation methods
- Leave-one-out(LOO) cross validation
- K-fold cross validation
- Multi-layer Perceptron
- Feed-forward neural network
- Unsupervised Learning
8. Artificial Intelligence(AI) (Day 81 – Day 100)
- Informed Search
- Uninformed Search
- Adversarial Search
- Propositional logic
- Predictive logic
- Reasoning under Uncertainty Topics
- Conditional Independence Representation
- Exact Inference through Variable Elimination
- Approximate Inference through Sampling
Conclusion
In the world of technology, Artificial Intelligence (AI) and Data Science stand as important pillars of innovation. The choice between AI and Data Science for your career path is not about choosing one over the other, but about understanding what your passion and strengths actually are. Whether you’re curious about the aspects of data interpretation or fascinated by the machines that can think and learn (Machine Learning), there are opportunities for every subject and interest.
If you wish to enroll into a course to learn Data Science and AI from scratch for your GATE 2024 exam, head over to Data Science and AI Full Course, which has been designed according the latest GATE 2024 syllabus.
100 Days of GATE Data Science & AI – FAQs
1. Can I go into AI with data science?
Yes, you can go into AI with data science. If you have a background in data science, going into AI is a great idea as data science and AI are closely related fields. But first, you need to build a strong base in data science and AI fundamentals.
2. What is the roadmap for data science?
Here is a brief roadmap for data science:
- Learn Python or R for programming and get a solid understanding of basic statistics and mathematics
- Master data manipulation with Pandas and develop exploratory data analysis (EDA) skills.
- Get hands-on experience with tools like Matplotlib for data visualization.
- Understand fundamental machine learning algorithm and learn model evaluation metrics.
- Understand hyperparameter tuningvery well.
- Develop skills in creating meaningful features.
- Then explore big data tools like Apache Spark.
- Go deep into advanced machine learning and deep learning.
- Choose a focus area like NLP or computer vision.
- Apply your skills in real-world projects and build a portfolio on platforms like GitHub.
- Stay updated with data science trends, read research papers and participate in communities.
- Improve your communication and problem-solving skills.
- Attend data science events and network with professionals for insights and opportunities.
3. How do I get started with data science and AI?
To get started with data science and AI, you need to start by mastering fundamental programming languages like Python. You also need to get a solid understanding of basic mathematics and statistics.
For a detailed answer, refer to Q2.
4. Is Data Science required for Artificial Intelligence?
Data science is closely related artificial intelligence (AI), and these two fields often intersect. Data science is not strictly required for Artificial Intelligence but it plays a key role in many AI applications.
5. Which is better- data science or machine learning?
There is no straightforward answer to this. This choice between data science and machine learning depends on your interests and career goals. Data science includes a broad range of activities, like exploring data, data analysis, and data interpretation to obtain insights while machine learning is a specialized field within data science that involves creating algorithms capable of learning from data and making predictions or decisions. While data science is about understanding and using data , machine learning is specifically focused toward creating predictive models. Both fields often overlap, and it is better to have a combination of skills from both data science and machine learning.
