Data Science Learning Roadmap for 2021

Data science has emerged as one of the fastest-growing and most in-demand skill sets in recent years. Companies in virtually every industry are looking for professionals who can glean valuable insights from data to drive smarter business decisions.

According to the U.S. Bureau of Labor Statistics, data science jobs are projected to grow by 31% between 2019 and 2029 – much faster than the average for all occupations. The median annual salary for data scientists is also very attractive, at $98,230 as of May 2020.

If you‘re intrigued by data science and ready to start building your skills in 2021, this learning roadmap will guide you through the key concepts, tools, and techniques you‘ll need to master. We‘ll cover:

• Foundational skills in programming, math, and statistics
• Data collection and wrangling
• Exploratory data analysis and visualization
• Machine learning algorithms and tools
• Advanced topics and specialized skills
• Tips for landing a data science job and continuing to learn throughout your career

Let‘s dive in!

Foundational Skills

Before you can start extracting insights from data, you‘ll need a solid grasp of a few foundational skill areas: programming, math, and statistics.

On the programming side, Python and SQL are must-learn languages. Python has become the most popular language for data science due to its extensive ecosystem of powerful libraries for data manipulation, analysis, and visualization. SQL is essential for querying relational databases, where much business data is stored.

Some key Python libraries to get familiar with include:

• NumPy for numerical computing
• pandas for data manipulation and analysis
• Matplotlib and Seaborn for data visualization

You‘ll also want to get comfortable with Git and GitHub for version control and collaboration.

There are tons of great free resources for learning these programming fundamentals, including:

• freeCodeCamp‘s Scientific Computing with Python and Data Analysis with Python certification courses
• Codecademy‘s Python track and Learn SQL course
• Khan Academy‘s Intro to SQL
• DataCamp‘s Git tutorial

To cement your programming skills, work through practice problems on sites like HackerRank and LeetCode, and build small projects like a calculator app, a web scraper, or a basic data analysis script.

In parallel to programming, you‘ll need a strong foundation in math and stats. Key concepts include:

• Linear algebra: vectors, matrices, matrix multiplication
• Calculus: derivatives, integrals, optimization
• Probability theory: probability distributions, Bayes‘ theorem, probability density functions
• Statistics: descriptive stats, hypothesis testing, Bayesian inference

You certainly don‘t need a math degree to get started with data science, but it helps to be comfortable with these essential concepts. Some great resources include:

• Khan Academy‘s Linear Algebra and Probability & Statistics courses
• MIT OpenCourseWare‘s Linear Algebra and Intro to Probability and Statistics courses
• José Unpingco‘s Python for Probability, Statistics, and Machine Learning book

Don‘t just read about these concepts passively – test your knowledge by working through problem sets and even tackling Project Euler‘s more math-heavy challenges.

Data Collection and Wrangling

With a solid base of programming and math knowledge, you‘re ready to start working with real-world data. Data collection and wrangling is all about sourcing data and getting it into a usable format for analysis.

Some common data sources include:

• Public datasets on government open data portals or sites like Kaggle
• Web pages that you can scrape with Python libraries like Beautiful Soup and Scrapy
• APIs that return data in JSON format, which you can access with Python‘s requests library
• SQL databases that you can query with Python‘s sqlite3 or other database connection libraries
• NoSQL databases like MongoDB
• Streaming data from sources like social media feeds or IoT sensors

Once you‘ve got some raw data to work with, you‘ll almost always need to do some cleaning and preprocessing using pandas and NumPy. This could include:

• Identifying and removing duplicate or irrelevant observations
• Fixing structural errors like typos or inconsistent capitalization
• Handling missing data by removing observations with missing values, filling in missing values, or using analysis techniques robust to missing data
• Merging data from multiple files or tables
• Reshaping data, like converting rows to columns or vice versa

Kaggle has some great hands-on data cleaning tutorials using real-world datasets:

• Data Cleaning micro-course
• Handling Missing Values challenge

To practice your web scraping chops, try pulling down sites like Wikipedia pages or IMDb movie listings into CSV files or a SQL database. You could also pick an API like the Twitter or Reddit API and build a tool to collect and store post data.

Exploratory Data Analysis and Visualization

Exploratory Data Analysis, or EDA for short, is a crucial step in the data science process. EDA is all about getting to know your data – its distribution, relationships between variables, and overall structure – through statistical analysis and visualization.

Some key things to look for in EDA include:

• Distributions of individual variables, including mean, median, mode, and outliers
• Relationships between variables, like correlations or associations for categorical data
• Missing data and potential sampling issues
• Interesting subsets or groupings within the data that warrant further analysis

Python libraries like pandas, Matplotlib, and Seaborn have great built-in functions for EDA, like:

• pandas‘ .describe() method for fast summary statistics
• Matplotlib‘s histogram, bar chart, and scatterplot functions for visualizing distributions and relationships
• Seaborn‘s regression plots, violin plots, and pair plots for more advanced visualizations

Other popular data viz tools include Plotly for interactive web-based plots, Bokeh for complex dashboards, and tools like Tableau and PowerBI for drag-and-drop analysis.

The best way to build EDA skills is to practice on a variety of datasets. Kaggle is a great resource here – check out their Data Visualization course and various EDA datasets to get started.

As you hone your EDA abilities, start challenging yourself to go beyond just analyzing the data to telling a story with it. What are the key takeaways or surprising insights you‘ve discovered? How can you communicate that through compelling data visualizations? Being able to wrangle data is important, but being able to extract and communicate meaningful insights from the data is what will make you truly effective as a data scientist.

Machine Learning

Machine learning is where a lot of the hype and excitement around data science comes from. At its core, machine learning is all about using algorithms to detect patterns in data and using those patterns to make predictions on new data.

Before diving into the algorithms, it‘s crucial to understand some key machine learning concepts:

• Supervised vs. unsupervised learning: In supervised learning, your data is labeled and the algorithm learns to predict the labels from input features. In unsupervised learning, the data isn‘t labeled and the algorithm aims to infer some underlying structure.
• Training vs. test split: Data is split into two subsets – a training set for the model to learn from, and a test set to evaluate how well it generalizes to unseen data.
• Underfitting & overfitting: Underfitting is when a model is too simple to capture the underlying pattern. Overfitting is when a model essentially memorizes the training data and doesn‘t generalize well. The goal is to find a middle ground.
• Key evaluation metrics: For supervised learning, metrics like accuracy, precision, recall, F1 score, and ROC curves are used to assess a model‘s performance. Unsupervised models use internal metrics like inertia or silhouette scores.

Some of the most essential machine learning algorithms to learn are:

• Linear Regression for predicting continuous outputs
• Logistic Regression for predicting binary outputs
• Decision Trees & Random Forests for both regression and classification
• K-Nearest Neighbors for classification
• K-Means for clustering unlabeled data
• Principal Component Analysis (PCA) for reducing the dimensionality of data

Python‘s Scikit-Learn library has all of these algorithms built in, with tons of options for tweaking parameters and assessing model performance. It‘s the best place to get started with applied machine learning.

If you‘re just getting into ML, freeCodeCamp has a great Machine Learning with Python certification course that will walk you through the fundamentals. Kaggle also has tons of tutorials and datasets to practice on, from beginner-level Titanic survivor classification to more advanced projects.

For a deeper dive into the theory behind these algorithms, check out Andrew Ng‘s classic Machine Learning course on Coursera. It uses the Octave programming language, but the core concepts will transfer to Python implementations.

As you gain confidence with these fundamental algorithms, you can branch out into more advanced topics like deep learning for unstructured data like image and text, or specialized approaches like time series forecasting and recommendation systems. fast.ai has a great (and free!) Deep Learning course that makes powerful techniques accessible even without extensive math background.

Advanced Topics

As you progress through your data science journey and start to specialize, there are a number of more advanced topics you can delve into:

• Natural Language Processing (NLP): Applying ML to text data for things like sentiment analysis, machine translation, text generation, etc. Key libraries are NLTK and SpaCy for general NLP, Gensim for topic modeling, and Hugging Face Transformers for deep learning on text.
• Computer Vision: Applying ML to image and video data for object detection, facial recognition, etc. OpenCV and Scikit-Image are good traditional libraries, while TensorFlow and PyTorch are the top deep learning tools.
• Big Data Processing: For truly massive datasets that don‘t fit in memory, you‘ll need tools like Apache Spark for distributed data processing. PySpark lets you leverage Spark with Python.
• Cloud Computing: Running ML workloads in the cloud offers flexibility and scalability. Key platforms are Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. They have managed ML services as well as support for running your own models on cloud infrastructure.

Pursuing these more advanced topics will likely involve a mix of taking specialized courses, reading academic papers, and lots of hands-on practice. Don‘t hesitate to do your own mini-research projects to test out a new method or dive deeper into an area you‘re curious about.

Landing a Data Science Job

Once you‘ve built up your core skills, you‘ll be in a great position to land a data science job. But skills alone aren‘t enough – you need to be able to effectively showcase your abilities and knowledge to potential employers.

Some tips for creating a standout portfolio:

• Build projects that showcase a range of skills, from data cleaning to machine learning to visualization
• Choose projects that you‘re genuinely passionate about – that enthusiasm will shine through
• Make your projects as end-to-end as possible, from data collection through insights and recommendations
• Include clear written explanations of your methodology and key findings – communication is key in data science roles
• Make your code clean, well-documented, and available on GitHub
• Consider creating a blog or website to showcase your thought process and tell the story behind your projects

Update your resume to highlight your relevant skills and projects, and optimize your LinkedIn profile to appeal to recruiters. Engage in the data science community by attending meetups, conferences, or webinars, and don‘t hesitate to reach out to data scientists you admire for an informational interview.

When it comes time for the job interview, be prepared to discuss your past projects in depth and explain key data science concepts. Brush up on SQL and coding basics in case you‘re asked to complete a technical screening. And have some questions prepared to ask your interviewers about the role and the company‘s approach to data science.

Getting your first data science job can take some persistence, but by continuously building your skills and portfolio, honing your communication abilities, and engaging in the community, you‘ll be well on your way to landing that dream role.

Never Stop Learning

In a field as rapidly evolving as data science, the learning doesn‘t stop once you land a job. Top data scientists make continuous skill development a key part of their careers.

Some strategies for ongoing learning:

• Set aside dedicated time each week to work through a course, read papers or blog posts, or experiment with a new technique
• Attend industry conferences like Strata Data Conference, KDD, or NeurIPS to learn from leaders in the field
• Participate in data science competitions on Kaggle or DrivenData
• Contribute to open source data science projects on GitHub
• Join online communities like Data Science Stack Exchange or the DataTau newsletter to stay up to date on the latest techniques and tools

It‘s an exciting time to be in data science. By following this learning roadmap and continuing to develop your skills over time, you‘ll be well-prepared to make valuable contributions in this dynamic and impactful field. Happy learning!