Data Science Roadmap 2024: Step-by-Step Journey to Success

Need for Data Scientist

The need for data science, and inherently the mastery of data science skills, has become increasingly important in today's world due to the vast amount of data being generated by businesses, organizations, and individuals. Data science provides the tools and techniques to extract meaningful insights from this data, enabling informed decision-making and has become essential for businesses to gain a competitive edge and improve their operations. It also plays a crucial role in addressing some of the world's most pressing challenges, such as healthcare, climate change, and social inequality. In short, the need for data science is vital in today's data-driven world to unlock the potential of data and make informed decisions.

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. To get started on your journey as a Data Scientist, check out our Data Science Bootcamp.

Key Tools for Data Science

Data science is a multidisciplinary field that relies on various tools and techniques to extract insights from data, including:

1. Programming languages: Python, R, and SQL 
2. Machine learning libraries: TensorFlow, Keras, and Scikit-learn 
3. Data visualization tools: Visualization tools like Tableau, Power BI, and Matplotlib 
4. Data storage and management systems: Databases like MySQL, MongoDB, and PostgreSQL 
5. Cloud computing platforms: AWS, Azure, and Google Cloud Platform

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

There are many resources available to learn Git and GitHub. For example, check out a Git tutorial here, or take Git and GitHub training here.

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.

Read More: A Data enthusiast, Jorge Mario Guzmán Olaya loves to stay on top of the data field by continuously upskilling. A Simplilearn fan, Olaya has taken more than 5 courses already with us, with the recent one being Data Science with R Certification Course. Read about his career journey and all the courses he’s taken with us in his Data Science Simplilearn Review.

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.

If you wish to learn the A-Z of data science from the top job roles to the top hiring companies, and in-demand skills to how to become a data science expert, explore our exclusive data science career resource page today!

Data Science Trends

Data Science is a growing field, and there are various trends that are shaping the future of the industry. AI and ML continue to be at the forefront of data science trends. They are used to automate tasks, develop predictive models, and improve decision-making. Big Data is also becoming more important, with organizations leveraging data from a wide range of sources, including social media, the Internet of Things (IoT), and sensors. Another significant trend is the use of DataOps, which involves the integration of agile methodologies and automation tools to streamline the data management process. Finally, there is a growing focus on ethics and responsible use of data, with increased attention being paid to issues such as privacy, bias, and transparency. As the data science landscape continues to evolve, it is likely that we will see further innovation in these and other areas.

Data Science Career Scope

Data Science offers a promising career scope with a high demand for professionals skilled in data analysis, machine learning, and statistics. With the exhaustive amount of data being generated, the career prospects for data scientists are expected to grow, with opportunities in a range of industries including healthcare, finance, and technology.

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.

Do You Want to Learn More About Data Science?

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!

Program Name	Data Scientist Master's Program	Post Graduate Program In Data Science	Post Graduate Program In Data Science
Geo	All Geos	All Geos	Not Applicable in US
University	Simplilearn	Purdue	Caltech
Course Duration	11 Months	11 Months	11 Months
Coding Experience Required	Basic	Basic	No
Skills You Will Learn	10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more	8+ skills including Exploratory Data Analysis, Descriptive Statistics, Inferential Statistics, and more	8+ skills including Supervised & Unsupervised Learning Deep Learning Data Visualization, and more
Additional Benefits	Applied Learning via Capstone and 25+ Data Science Projects	Purdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Resume Building Assistance	Upto 14 CEU Credits Caltech CTME Circle Membership
Cost	$$	$$$$	$$$$
	Explore Program	Explore Program	Explore Program

Data science has become integral to today's IT landscape, influencing everything from data mining to machine learning. If you'd like to enter a career in data science, Simplilearn has everything you need to make your data science roadmap journey easier.

Simplilearn’s Caltech CTME Data Science Bootcamp, run in partnership with IBM, features masterclasses by distinguished Caltech instructors and IBM experts and features exclusive hackathons and Ask Me Anything sessions run by IBM.

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.

FAQs: 

1. What is the career path of a data scientist?

The career path of a data scientist typically involves acquiring skills in data analysis, statistics, machine learning, and programming, and working in industries that require data-driven insights. To get a detailed career path, download our data science career guide today.

2. Can I learn data science on my own?

Yes, you can learn data science on your own through our online resources and courses.