The sexiest job of the 21st century, according to Harvard Business Review (HBR), was that of a data scientist in 2012. After reviewing their claim 10 years later, HBR found that the demand for data scientists has grown even more than anticipated due to the increasing acceptance of Artificial Intelligence (AI) in the business world. According to the U.S. Bureau of Labor Statistics, job opportunities for data scientists are expected to increase by 36% between 2021 and 2031, which is much faster than the average for all occupations. This demonstrates how data science is undoubtedly among the most exceptional fields. However, becoming a data scientist is as challenging as it is rewarding. Here’s a data science roadmap with a comprehensive, step-by-step guide to help you become a successful data scientist.
What is a Data Science Roadmap?
A data science roadmap is a strategic plan that outlines the essential steps, skills, and knowledge required for aspiring data scientists to succeed in the vast and multidisciplinary field of data science. Given the wide range of techniques and tools for analyzing and interpreting data, many aspiring data scientists can find the field challenging to navigate. This is where a data science roadmap comes into play; it provides you with a clear blueprint to help prioritize learning and focus on the most important areas of data science. With a structured approach toward achieving career goals, aspiring data scientists can pursue their dreams and succeed in their careers as data scientists.
Roadmap for Data Science
Step 1: Learning About Programming or Software Engineering
A strong foundation is essential for building a successful and sustainable career. This foundation can be established for data scientists through proficiency in computer science and programming languages. It is crucial for a data scientist to have a sound understanding of the following programming languages and computer science concepts:
- Data structures
- Web Scraping
- Object-oriented programming
Step 2: Learning About Data Collection and Cleaning
To produce valuable insights, data science heavily relies on the accuracy and reliability of data. However, if the data collected is inaccurate, incomplete, or contains errors, the produced insights may be misleading. Data scientists collect data from various sources that may use different formats and structures and then clean it by identifying and correcting errors, eliminating duplicates, and standardizing the data. Thus, in order to achieve high-quality analysis, aspiring data scientists should know how to collect and clean data efficiently. Some data cleaning tools include Microsoft Excel, OpenRefine, and Trifacta.
Step 3: Learning About Business Acumen, Exploratory Data Analysis, and Storytelling
Data scientists play a critical role in helping organizations make informed decisions by providing valuable insights from data. To achieve this, data scientists must be proficient in business acumen, exploratory data analysis, and storytelling. Exploratory data analysis involves analyzing data to identify patterns, trends, and outliers using techniques like summarization, filtering, and statistical methods.
However, simply understanding the data is not enough: Data scientists should also have a clear understanding of the business context and the problem they are trying to solve, which is where business acumen comes into play. Lastly, data scientists need to be able to effectively communicate their findings to nontechnical audiences using data visualization and storytelling techniques.
Step 4: Learning More Advanced Math, DL, and Deployment
Data science professionals can benefit greatly from developing skills in advanced math, deep learning, and deployment. In addition to increasing their chances of securing desirable jobs, possessing these skills can also enhance their problem-solving abilities and enable them to deliver impactful solutions. Here are some key topics that you can consider learning:
- Linear algebra
- Analytic Geometry
- Density Estimation
- TensorFlow and Keras
- Artificial Neural Network
- Recurrent Neural Network
- Convolutional Neural Network
- A Single Neuron
- Binary Classification
- Microsoft Azure
- Google Cloud Platform
Step 5: Acquiring the Key Tools for Data Science
Here are some data science tools and concepts that you need to be familiar with in order to carry out your work efficiently:
- Probability: This includes key concepts such as 1D random variable, discrete distribution, and continuous distribution
- Statistics: Understanding computer simulation, data description, graphs, and sampling distribution
- Data visualization tools: Knowledge of popular tools such as Excel VBA, Tableau, Qlik Sense, and Power BI, to effectively present data
Step 6: Learning About Machine Learning and AI
AI and Machine Learning (ML) have brought significant advancements across various industries, such as finance, healthcare, automobile, and retail. These technologies are transforming the way things are done. Consider exploring AI and ML concepts such as reinforcement, unsupervised, and supervised learning. Data scientists who are proficient in AI and ML can bring great value to their organizations by leveraging these technologies to solve complex problems, make data-driven decisions, and develop innovative solutions.
Track Your Learning Progress
Finally, keep track of your learning progress to identify areas that require more attention. It will also assist you in staying motivated, accountable, and focused. Don’t lose sight of your goals or feel discouraged by the extensive data science roadmap. While the journey to success in the field of data science may be long and challenging, it is certainly achievable.
Acquire Data Science Skills and Knowledge with Emeritus Certification Courses
This guide offers a comprehensive overview of the data science roadmap to help you get started and pave your way toward success in this rewarding field. If you are looking for more in-depth learning, explore these data science courses offered by Emeritus. By following a well-structured roadmap, these courses can equip you with a clear understanding of the data science fundamentals, skills, and tools to build a strong foundation of knowledge and help you reach your ultimate goal.
This content was originally published by Manasa Ramakrishnan on the Emeritus Online Learning Review.
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