Data science is a solid, rapidly growing field with plenty of untapped potentials. LinkedIn's Emerging Jobs Report shows that the market is expected to grow significantly over seven years, going from $37.9 billion in 2019 to $230.80 billion by 2026.
Consequently, aspiring IT professionals interested in a long-lasting career should consider data science their landing spot. However, learning a new discipline can be challenging. The difficulty can be mitigated by creating and implementing a solid educational plan, in other words, a roadmap.
This article presents all the information needed create a data science roadmap for 2023. We will explain what a data science roadmap is, the various components and milestones in a data science roadmap, tracking your progress on the roadmap for data science, and other related resources.
We start with the fundamentals. What is a data science roadmap?
What Is a Data Science Roadmap?
The easiest way to handle this question is by first defining the term “roadmap.” Roadmaps are strategic plans that determine a goal or the desired outcome and feature the significant steps or milestones required to reach it.
On the other hand, data science, according to this article, is:
“…a field that deals with unstructured, structured data, and semi-structured data. It involves practices like data cleansing, data preparation, data analysis, and much more.
Data science is the combination of statistics, mathematics, programming, and problem-solving; capturing data in ingenious ways; the ability to look at things differently; and the activity of cleansing, preparing, and aligning data.”
Therefore, a data science roadmap is a visual representation of a strategic plan designed to help the aspiring IT professional learn about and succeed in the field of data science.
Let’s take a close look at this roadmap for data science.
Learning About Programming or Software Engineering
As you begin your data science journey, you must have a solid foundation. The data science field requires skill and experience in either software engineering or programming. You should learn a minimum of one programming language, such as Python, SQL, Scala, Java, or R.
Programming Topics to Include
Data scientists should learn about common data structures (e.g., dictionaries, data types, lists, sets, tuples), searching and sorting algorithms, logic, control flow, writing functions, object-oriented programming, and how to work with external libraries.
Additionally, aspiring data scientists should be familiar with using Git and GitHub-related elements such as terminals and version control.
Finally, data scientists should enjoy a familiarity with SQL scripting.
Also Read: How to Become a Data Scientist in 2022?
Learning Git and GitHub
Problem Solving and Project Building
Once you have acquired a functional familiarity with the above concepts, apply your new knowledge by tackling building projects such as writing Python scripts that perform data extractions or creating a simple web app that blocks undesirable websites.
You can also check out this article to learn more about problem solving.
Learning About Data Collection and Cleaning
Data scientists are often required to find appropriately valuable data that solves problems. They collect this data from many different sources, including APIs, databases, publicly available data repositories, and even scraping if the site permits it.
However, the data gathered from these sources is rarely ready to use. Instead, it needs to be cleaned and formatted before it's used, using tools such as a multi-dimensional array, data frame manipulation, or employing scientific and descriptive computations. Data scientists typically use libraries like Pandas and NumPy to help turn the information from raw, unformatted data to ready-to-analyze data.
Selected Data Collection Projects
Practice makes perfect, so try choosing a publicly accessible data set, develop a set of questions related to the dataset’s domain, then practice data wrangling with Pandas or NumPy to get the answers.
Alternately, gather data from a website or API (such as quandl, TMDB, Twitter API) that allows public consumption and transform the information to be stored from different sources into an aggregated database table or file.
How You Can Learn About Business Acumen, Exploratory Data Analysis, and Storytelling
Time to move on to the next stage of your data science roadmap: data analysis and storytelling. Data analysts, who share a strong affinity with data scientists, draw insights from data, then relay their findings to management in easy-to-understand terms and visualizations.
As they relate to storytelling, the above responsibilities require proficiency in data visualization (plotting data using libraries like plotly or seaborn) and strong communication skills. In addition, you should learn:
- Business acumen: Practice asking questions that target business metrics. Additionally, practice writing concise and clear reports, business-related blogs, and presentations.
- Dashboard development: This subject entails using Excel or specialized tools such as Power BI and Tableau to construct dashboards that summarize or aggregate data that helps management make informed actionable decisions.
- Exploratory data analysis: This knowledge covers defining questions, formatting, filtering, handling missing values, outliers, and univariate and multi-variate analysis.
A Data Analysis Project
Conduct an exploratory analysis of movie datasets and devise a formula to create profitable movies, using data from past censuses or financial/health/demographic databases.
Why You Need to Learn About Data Engineering
Data engineering supports the Research and Development teams by ensuring that clean data is readily available for research engineers and scientists at major data-driven organizations. Although data engineering is an entirely different field, you have the option of bypassing this section if you plan to focus chiefly on the statistical side of things.
Data engineer responsibilities include constructing efficient data architectures, streamlining data processing, and monitoring and maintaining large-scale data systems. Data engineers use SQL, Shell (CLI), and Python/Scala tools to automate file system tasks, build Extract/Transform/Load pipelines, and optimize database operations into a high-performance resource.
Finally, data engineers are often responsible for implementing these data architectures, which inevitably requires proficiency in cloud service providers like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, among others.
Here’s a Data Engineering Certification
Consider this Data Engineering Bootcamp to give you the skills to build data warehouses, design data models, automate pipelines, and work with massive datasets.
How You Can Learn About Applied Statistics and Mathematics
Statistical methods are an integral part of data science, where most data science interviews focus on inferential and descriptive statistics. Mathematics and statistics smooth the road to a better understanding of how algorithms work.
Therefore, at this stage of your data science roadmap, you should focus on mastering the following:
- Descriptive Statistics: Learn about location estimates (mean, median, mode, trimmed statistics, and weighted statistics), and variability used to describe data.
- Inferential statistics: This form of statistics involves defining business metrics, A/B tests, designing hypothesis tests, and analyzing collected data and experiment results using confidence intervals, p-value, and alpha values.
- Linear Algebra and Single and Multi-Variate Calculus: These subjects help you better understand gradient, loss functions, and optimizers used in machine learning.
Statistics Project Ideas
Analyze figures like stock prices or cryptocurrency values, then design a hypothesis around the average returns or another metric of your choice. Finally, use critical values to determine whether you can reject the null hypothesis.
Design and conduct small experiments with your associates by having them answer a question or interact with an app or answer. Then, run statistical methods on the data once you have gathered a healthy amount over a designated period.
Wrapping It Up by Learning About Machine Learning and AI
As you approach the end of your data science roadmap, it’s time to conclude your trip by learning about two fields that heavily rely on data science: Artificial intelligence and Machine Learning. These topics fall into three categories:
- Reinforcement Learning: This discipline helps you build self-rewarding systems. If you want to understand reinforcement learning, learn how to optimize rewards, create Deep Q-networks, and use the TF-Agents library, to name a few.
- Supervised Learning: This discipline covers regression and classification problems. It would help if you studied simple linear regression, logistic regression, multiple regression, KNNs, polynomial regression, naive Bayes, tree models, and ensemble models. Round out your studies by learning about evaluation metrics.
- Unsupervised Learning: Unsupervised learning features applications such as clustering and dimensionality reduction. Take deep dives into hierarchical clustering, K-means clustering, PCA, and gaussian mixtures.
Resources to Teach You About Machine Learning
There are plenty of ideal resources out there that can teach you about machine learning. Consider picking up this book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition.
Or, if you want some high-quality intense learning, take Caltech Machine Learning bootcamp. This AI/ML bootcamp teaches Statistics, Python, Machine Learning, Deep Learning, Natural Language Processing, and Supervised Learning.
Track Your Learning Process
If you are undertaking a long-term involved project such as learning data science, you must have a means of tracking your progress. This way, you know what you've already covered, preventing wasteful redundancy, and you can better visualize what you need to do next.
Here’s a learning tracker you can use to monitor your progress and keep yourself organized.
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Do You Want to Learn More About Data Science?
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The program covers vital data science topics such as Python programming, R programming, machine learning, deep learning, and data visualization tools via an interactive learning model that includes live sessions by global practitioners and practical labs.
According to Glassdoor, data scientists earn an annual average of $120,256. The world needs more data scientists and is willing to offer attractive incentives and a stable, secure career. If this sounds like your kind of profession, check out Simplilearn and take those first few steps towards a new career. Visit Simplilearn today!