Data Science Curriculum for Beginners

Data Science Curriculum for Beginners

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6 min read

๐Ÿ” I am currently working as a Sr Data Scientist in a Fortune 50 company. Before this, I worked in multiple startups as a Data Analyst and a Data Scientist. I have given more than 30 Data interviews ๐Ÿ’ป in the last 4 years and have also conducted technical rounds for entry-level data scientists.

โฐ I spent an enormous amount of time learning everything listed in popular curriculums but some topics were not expected for entry-level jobs which can even make you lose interest in studying as it takes a lot of time. A sizeable portion of the curriculums available made sense to learn either while working as a Data Scientist or when you want to crack an interview for senior roles. ๐Ÿ˜Š

With that in mind, I want to create a simple and easy-to-follow curriculum that can help freshers venture into the Data Scientist field as soon as possible.

The curriculum -

โœ… Can help you crack 95% of interviews for entry-level jobs in the Data Science field
โœ… Can help you start a career in a non-FAANG company for a base salary >$95k๐Ÿ’ฐ
โœ… Can be completed in 3 monthsโŒ›๏ธ
โœ… Can help you switch from a non-technical role as soon as possible

We will approach the learning process through 4 stages: Crawl, Walk, Run, and Fly

Crawl ๐Ÿšผ

Python Basics ๐Ÿ

  • Start by learning the fundamentals of the Python programming language, focusing on concepts relevant to data science.

  • Understand data types, variables, conditional statements, loops, and functions in Python.

Data Structures ๐Ÿ“š

  • Dive into data structures in Python, with a specific focus on lists and dictionaries.

  • Explore their properties, learn how to manipulate and access elements, and understand when to use each data structure.

Pandas and NumPy ๐Ÿผ

  • Learn to load data into Pandas DataFrames, perform basic data manipulation tasks, and handle missing values using Pandas

  • Utilize NumPy for numerical computations, working with arrays, and performing mathematical operations.

Data Visualization (Matplotlib) ๐Ÿ“Š๐Ÿ“ˆ๐Ÿ“‰

  • Explore different plot types, such as line plots, bar plots, scatter plots, and histograms.

  • Learn to visualize data using the Matplotlib library in Python.

  • Learn how to customize plot aesthetics, add labels, titles, and legends to enhance visual communication.

  • Practice creating visually appealing plots using Matplotlib to effectively present insights from data.

Explore Jupyter Notebook and Jupyter Lab ๐Ÿงช

  • It allows you to write and execute code interactively. This interactivity makes it easier to experiment, test code snippets, and see immediate results.

SQL Essentials ๐Ÿ‘ฉโ€๐Ÿ’ป

  • Basic SQL Querying ๐Ÿ—„๏ธ๐Ÿ’ก

    • Master the fundamental SQL operations for data retrieval from relational databases.

    • Understand SELECT, FROM, WHERE, GROUP BY, ORDER BY, HAVING, and WITH clauses.

  • Essential SQL Functions ๐Ÿ“Š๐Ÿ”ข

    • Understand COUNT, MIN, MAX, DISTINCT, SUM, IF, and IFNULL.

    • Utilize these functions to perform data aggregations and transformations.

Math Fundamentals ๐Ÿงฎ (Hands-on using Python Libraries)

  • Learn how to measure the central tendency of a variable using mean, median, and mode

  • Learn the concepts behind variance and standard deviation as measures of dispersion.

  • Learn how to measure the relationship between variables using Correlation and Covariance


Walk ๐Ÿšถ๐Ÿผ

Machine Learning 101 with Python ๐Ÿค–

  • Linear Regression

  • Logistic Regression

  • Feature Engineering: Learn techniques for data preprocessing, handling missing values, scaling data and feature selection.

  • Learn how to measure the success of the ML model using performance metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared (R^2), Precision, Recall, Confusion Matrix, F1 Score, ROC-AUC.

Advanced Math ๐Ÿค“

  • Hypothesis Testing ๐Ÿ”

    • Learn hypothesis testing techniques: t-tests, z-tests, ANOVA, and Chi-Square tests. ๐Ÿ“Š๐Ÿ”

    • Understand when and how to apply these tests using Python for data analysis and making inferences. ๐Ÿ๐Ÿ“

    • Grasp concepts like null and alternative hypotheses, p-values, and confidence intervals. ๐Ÿง ๐Ÿ’ก

  • Statistics ๐Ÿ“Š

    • Explore key statistics concepts: random variables, probability distributions, and measures of variance. ๐Ÿ“ˆ๐Ÿ“Š

    • Study various probability distributions (normal, binomial, exponential) and work with them in Python. ๐ŸŽฒ๐Ÿ

    • Gain knowledge in inferential statistics, including sampling distributions and confidence intervals. ๐Ÿ“š๐Ÿ”

  • Probability (central limit theorem, Bayes's theorem)

    • Understand the central limit theorem's significance and its connection to sampling distributions. ๐Ÿ“ˆ๐ŸŽฒ

    • Dive into Bayes's theorem and its applications in conditional probability and Bayesian statistics. ๐Ÿ“š๐Ÿ”

Advanced SQL ๐Ÿ’ช

  • Joins

    • Master different types of SQL joins: LEFT JOIN, RIGHT JOIN, CROSS JOIN, INNER JOIN, and UNION ALL. ๐Ÿ’ช๐Ÿ”—

    • Understand when and how to use each type of join to combine data from multiple tables. ๐Ÿ“Š๐Ÿ—ƒ๏ธ

  • Window Functions

    • Dive into window functions in SQL, including RANK, DENSE_RANK, and ROW_NUMBER. ๐Ÿ“Š๐Ÿ”ข

    • Learn how to use window functions to perform calculations and analyze data within specified partitions or windows. ๐ŸชŸ๐Ÿ”


Run ๐Ÿƒโ€โ™‚๏ธ

Advanced Machine Learning Concepts ๐ŸŽฏ

Algorithms

  • Supervised Learning Algorithms:

    • Ridge and Lasso

    • Decision Trees

    • Random Forest

    • XGBoost

    • K Nearest Neighbors

    • Support Vector Machines

  • Unsupervised Learning Algorithms:

    • K Means Clustering

    • Principal Component Analysis

    • DBSCAN (Optional)

  • Time Series Forecasting Algorithms:

    • Autoregressive Integrated Moving Average (ARIMA)

    • Exponential Smoothing Methods

    • Prophet (developed by Facebook)

Handling Advanced ML Models

  • Learn techniques to handle overfitting and underfitting, which are common challenges in machine learning. โš–๏ธ๐Ÿ“Š

  • Understand strategies for dealing with unbalanced data, where the classes are not evenly distributed. โš–๏ธ๐Ÿ”

  • Learn how to implement Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. ๐Ÿ“š๐Ÿ”ข

Portfolio Project

  • Work on a portfolio project that demonstrates your understanding of advanced machine learning concepts. ๐Ÿš€๐Ÿ’ผ

  • Use Kaggle projects as references or find real-world datasets to solve interesting machine-learning problems. ๐Ÿ“Š๐Ÿ’ป

  • Create a machine learning boilerplate code in Jupyter Notebook that can be used for take-home assignments and ML coding rounds. ๐Ÿ’ก๐Ÿ”


Fly ๐Ÿš€

  • Cloud Services for ML Model Deployment

    • Familiarize yourself with any one cloud service providers like Google Cloud, AWS, and Azure for deploying ML models. ๐ŸŒฉ๏ธโ˜๏ธ

    • Gain practical experience by deploying models and study real-world examples and case studies of ML model deployment. ๐Ÿš€๐Ÿ’ป

    • Learn about model versioning and monitoring in production to ensure smooth and efficient deployment. ๐Ÿ”„๐Ÿ”

  • Practicing Coding Skills

    • Practice solving Easy and Medium category Python and SQL questions on platforms like LeetCode. ๐Ÿ๐Ÿ’ป

    • Strengthen your coding abilities and problem-solving skills by tackling a variety of coding challenges. ๐Ÿง ๐Ÿ’ก

  • Creating a Compelling Resume using STAR Method

    • Utilize the STAR method to structure your resume and effectively communicate your experiences and achievements. ๐ŸŒŸ๐Ÿ“

    • Provide concise and compelling examples using the Situation, Task, Action, and Result framework to showcase your skills and accomplishments. ๐Ÿ“„๐Ÿ’ผ

  • Reaching Out and Applying on LinkedIn

    • Leverage the power of LinkedIn to network and explore job opportunities in the data science field. ๐Ÿค๐Ÿ”

    • Connect with professionals, join relevant groups, and actively engage with the data science community. ๐Ÿ’ผ๐ŸŒ

Throughout your journey, focus on continuous learning and improvement. Stay up to date with the latest advancements in the field and leverage online resources, tutorials, and courses to enhance your skills. Best of luck in your data science endeavors! ๐ŸŒŸ๐Ÿ“š

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