Curious about what a regular data science course would cover or teach you? Well, look no further. Data science happens to be one of the hottest topics these days, and its training programs are fully loaded with knowledge that will not only get you job-ready but also prepare you for some of the toughest data science interview questions. In this post, we’ll discuss some topics you can expect in a comprehensive data science course.
Commonly Covered Topics in Data Science Training
While the topics covered in data science trainings vary, here are some of the most commonly covered topics:
1) Data Structures and Algorithms
A good data science course always begins with the basics, such as Data Structures and Algorithms. These are basically the stepping stones toward effective problem-solving methodologies.
Sorting Algorithms
Understanding different sorting algorithms would be a part of any data enthusiast’s knowledge base. You’ll progress hands-on with the different algorithms, starting from Merge Sort to Quick Sort and Heap Sort. You’ll learn how to decide on the best algorithm in various situations. They are also going to teach you about asymptotic analysis, which is the notation that measures the complexity of time and space.
| Sorting Algorithm | Time Complexity | Space Complexity |
| Merge Sort | O(n log n) | O(n) |
| Quick Sort | O(n log n) | O(log n) |
| Heap Sort | O(n log n) | O(1) |
Recursion and Backtracking
Recursion is a subtle yet powerful tool. You will learn how to solve complicated problems by breaking them down into simpler sub-problems, just like a lazy manager would delegate tasks. You will master backtracking and exhaustive enumeration so you can be ready in coding interviews, especially in those tree or graph problems.
2) Trees and Graphs
Trees and graphs are two favorite data structures for any data scientist. They are versatile in solving many problems, including searching and pathfinding.
Binary Trees & Graphs
Trees and graphs form the basis for understanding data science. Data science courses will cover BST and their operations-like insertion, deletion, and traversal. Apart from simple data structures, data science courses will also cover some advanced ones like AVL Trees and Red-Black Trees.
In the world of graphs, you will get to learn graph traversal algorithms that include Breadth-First Search (BFS) and Depth-First Search (DFS). Graph problems crop up pretty often in the data science interview questions, so a word of preparation: be ready to ace those.
| Operation | Binary Tree | Graph |
| Search | O(log n) | O(V+E) |
| Insertion | O(log n) | O(V+E) |
| Traversal (DFS/BFS) | O(n) | O(V+E) |
3) SQL Programming
Now, let’s get real. Almost all best data science course in reality cover SQL programming because data scientists deal with databases all the time.
In this module, you will be covered with joins, subqueries, and analytical functions such as LEAD() and LAG() that help analyze time-series data for businesses. For instance, year-over-year growth calculation of a company, or ranking products about sales performance.
4) Probability and Statistics
Probability is a language of uncertainty; statistics is grammar. And for a data scientist, it is an essential skill to master. First, the Probability section will cover topics like Conditional Probability, Bayes’ Theorem, and Combinatorics. The common examples will include Coin Toss, Dice Roll, and Card Problems that set you up to ace data science interview questions commonly asked at companies like FAANG.
Example:
- Suppose you flip a coin 100 times. What’s the probability that you will get more than 60 heads?
Distributions
You’ll look at several probability distributions: Normal, Binomial and Poisson. Being familiar with how and when to use each of these distributions to model real world situations is an essential skill if you want to be a data scientist. You’ll find out how the Central Limit Theorem is used in Finance and Marketing to make predictions about means for very large samples.
| Distribution | Real-World Application |
| Normal | Stock Market Predictions |
| Binomial | Customer Conversion Rates |
| Poisson | Website Traffic Modeling |
Dynamic Programming (DP)
Dynamic programming teaches you how to break problems down, solve them recursively, and then store results for efficiency. Common data science interview questions often involve DP-based problems like finding the shortest path, or optimizing some sort of resource allocation model.
DP Techniques
You will practice techniques such as Top-Down Memorization and Bottom-Up Tabulation in order to reduce time complexity. For example, the Knapsack Problem becomes trivial with DP in your toolset.
5) Data Science Design: A/B Testing
A fundamental comprehension of A/B testing and how companies around the world can base decisions on it in data science is considered paramount. For instance, you may work at Google, and they want to test the new search algorithm. You will learn how to set up hypothesis tests, choose between T-tests and Chi-square tests, and how to calculate metrics such as p-values and confidence intervals.
One potential interview question could be: How would you test whether a new ad placement increases user engagement on a website?
| Metric | Purpose |
| AUC-ROC | Evaluating classification models |
| Precision & Recall | Assessing prediction accuracy |
| p-value | Hypothesis testing |
6) Supervised and Unsupervised Machine Learning
Next come the big guns – Machine Learning (ML).
Supervised ML
In Supervised ML, you feed in some data that is labeled into the algorithm and let it learn. You will be learning models such as Logistic Regression, k-Nearest Neighbors (kNN), and Support Vector Machines (SVM).
If you go through data science interview questions in your preparation, you’ll commonly see questions such as:
- Why is Naive Bayes called naive?
- How do you handle overfitting in a Decision Tree?
Unsupervised ML
In Unsupervised ML, you would learn how to employ algorithms like K-Means Clustering and DBSCAN to cluster similar data together. You will also be working with Dimensionality Reduction techniques like PCA, Principal Component Analysis, to understand how one should go about reducing the number of variables in a dataset without much loss in information.
| Algorithm | Type | Key Concept |
| Logistic Regression | Supervised | Predicts categorical outcome |
| K-Means Clustering | Unsupervised | Groups similar data points |
| PCA | Dimensionality Reduction | Reduces dataset features |
7) Deep Learning
The final topic is Deep Learning: the rocket science of data science. The topic will introduce you to Neural Networks, including both CNN for image processing and RNN for sequential data such as time series. One key takeaway is how backpropagation enables these networks to “learn” from their mistakes, and interviewers just love asking about this!
