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Data Science Road map

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Data science is a 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 aspiring IT professionals learn about and succeed in the field of data science.

The right skills can ensure that you thrive in your education and career, and take advantage of one of the fastest-growing industries in India.

Data scientists are one of the most important and highest-paying progressions of the digital age. Not everyone can simply just decide to pursue this career. You need to have certain technical and soft skills. It is important to know about them before you decide to pursue data science.

Must have Skills

“Data science” the first word itself speaks a lot i.e, Data, without which this field is nothing and data = statistics. There are so many skills to acquire , the list may be infinite but listing out some

[ ‘ Statistics’ , ‘ mathematics’ , ‘programming’ ,’ machine learning’ ,’ domain expertise’ ,’ story telling’ , …………… ]

Let’s take a close look over these skills one by one.

1. Statistics

One of the crucial aspects of the data science approach is the process of extracting and interpreting data. When data is extracted we develop perceptions or best to say cultivating possibilities out of that extracted data. In data science, these possibilities are interpreted with the help of statistics and the term is known as statistical analysis.

When the data is converted into a numerical form, it provides us with interminable possibilities to interpret the information out of it.  Statistics is the key to extracting and processing data and bringing successful results. Detecting structure in data, large or small and making predictions are critical stages in data science that can either make or break research. Statistics provides measures and methods to evaluate insights out of data by getting the right mathematical approach for data.

One can start from here |

Introduction to Statistics (Coursera ) Elements of Statistical Learning (book)

2. Mathematics

Mathematics is an integral part of data science. Any practicing data scientist or person interested in building a career in data science will need to have a strong background in specific mathematical fields. 

While it is possible to get into data science without fully understanding mathematics at its core, a truly effective and versatile data scientist should have a solid foundation in math

some of the most popular applications of math that you will use as a data scientist and many of us use already……

3. Programming

You need to have programming skills to become a data scientist. From developing data models to creating analytical models, many functions in data science require programming, so knowing one or more programming languages is going to be important.

However, knowing just any programming language isn’t going to help. You need to know either Python, R, Java, or SQL if you want to do well in data science. You can also benefit from knowing about programming packages and libraries like TensorFlow.

one can start from here |

Google Data Analytics Professional Certificate (Coursera)

Data Science Fundamentals with Python and SQL (Coursera)

3. Machine learning

The idea behind Machine Learning is that you teach and Train Machines by feeding them data and defining features. Computers learn, grow, adapt, and develop by themselves when they are fed with new and relevant data, without relying on explicit programming. Without data, there is very little that Machines can learn. The Machine observes the dataset, identifies patterns in it, learns automatically from the behavior, and makes predictions.

Machine Learning analyzes large chunks of data automatically. Machine Learning basically automates the process of Data Analysis and makes data-informed predictions in real-time without any human intervention. A Data Model is built automatically and further trained to make real-time predictions. This is where the Machine Learning Algorithms are used in the Data Science Lifecycle.

one can start from here |

Machine Learning Specialization (Coursera) Machine Learning Yearning (Book)

4. Domain Expertise

Domain expertise is the knowledge and understanding of a particular field. As data scientists, you may be working in a wide variety of industries, each of which has its own intricacies that can only be learned gradually over time.

It is impossible to be a domain expert in everything related to data, but the truth is that data scientists need to be prepared for the many industries that are now embracing data-driven practices. This makes the role of domain knowledge in data science more important than ever.

5. Storytelling

“Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.” — Chip and Dan Heath 

to be precise ” DATA storytelling” can be defined as the process of using data to tell a story. It can include elements like data visualization, data analysis, and storytelling techniques such as narratives or scenarios as discussed in the previous section. The goal is to make data more understandable for non-technical users by presenting it in an appealing manner with relevant context attached.

This might sound a bit vague but in a data science’s life cycle data storytelling is as important as any other aspect like data cleaning, preprocessing, etc.

It can be used to educate, inform or persuade an audience. It is a way to convey data-driven stories, as it enhances engagement and stimulates curiosity among viewers. There are various different data visualization tools that allow data storytellers to animate seemingly static data into eye-catching infographics. Data storytelling is a synonym for visualizations. Dashboards tell data stories. Which will be the topic of our next blog. until then …..happy reading!

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