Data science road map

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 su...

In this roadmap for data science learning, you can understand different verticals for data science and the areas you need to focus on if you are getting started with data science. Here is the Data Science roadmap diagram. Data Science Roadmap For Beginners Let’s look at each vertical and related resource to get started. Learn Programming Languages It is very important to master a programming language related to data science. Following are the recommended resources to get started. • • • • • • Learn Data Analysis Data analysis is a very important vertical in data science. it helps in finding meaningful data by analyzing multiple sources of data. The data source and type of data could be different from org to org. Normally data collection happens through multiple sources to form a data lake. There are many data management strategies used based on data classification. Following are the recommended tracks to get started with data analysis. • • • • • Learn Data Visualisation Data visualization transforms massive amounts of processed data into meaningful visuals for the key stakeholders in the business. A business analyst can make use of this useful information to find opportunities for business growth. The following are good resources to get started with data visualization. • • • • Learn Machine Learning For all predictive analyses, machine learning is the key part. ML deals with data Classification, Regression, reinforcement learning, Deep learning, Dimensionality Reduction, Cl...

According tothe Harvard Business Review ,Data Scientist is “The Sexiest Job of the 21st Century”. Is this not enough to know more about data science! In the world of data space, the era of Big Data emerged when organizations are dealing with petabytes and exabytes of data. It became very tough for industries for the storage of data until 2010. Now when popular frameworks like Hadoopand others solved the problem of storage, the focus is on processing the data. And here Data Science plays a big role. Nowadays the growth of data science has been increased in various ways and so one should be ready for the future by learning what data science is and how can we add value to it. What is Data Science? So now the very first question arises is, “ What is Data Science ?” Data science means different things for different people, but at its gist, data science is using data to answer questions. This definition is a moderately broad definition, and that’s because one must say data science is a moderately broad field! Data science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information. So briefly it can be said that Data Science involves: • Statistics, computer science, mathematics • Data cleaning and formatting • Data visualization Nowadays it is known to everyone that how popular is Data Science. Now the questions that arise are, Why Data Science (Decide the Goal First?), how to start? Where t...

Although nothing really changes but the date, a new year fills everyone with the hope of starting things afresh. If you add in a bit of planning, some well-envisioned goals, and a learning roadmap, you'll have a great recipe for a year full of growth. This post intends to strengthen your plan by providing you with a learning framework, resources, and project ideas to help you build a solid portfolio of work showcasing expertise in data science. Just a note: I've prepared this roadmap based on my personal experience in data science. This is not the be-all and end-all learning plan. You can adapt this roadmap to better suit any specific domain or field of study that interests you. Also, this was created with Python in mind as I personally prefer it. What is a learning roadmap? A learning roadmap is an extension of a curriculum. It charts out a multi-level skills map with details about what skills you want to hone, how you will measure the outcome at each level, and techniques to further master each skill. My roadmap assigns weights to each level based on the complexity and commonality of its application in the real-world. I have also added an estimated time for a beginner to complete each level with exercises and projects. Here is a pyramid that depicts the high-level skills in order of their complexity and application in the industry. Data science tasks in the order of complexity This will mark the base of our framework. We’ll now have to deep dive into each of these strata...

Introduction As organizations are generating and storing more and more data, they are looking to hire professionals who can dig into this overwhelming amount of Data to derive valuable insights that can help drive business decisions. This has led the demand for Data Scientists to surge in the past few years. Data Scientist are one of the highest-paid professionals across the industries, and Data Science offers a promising and lucrative career path. As per LinkedIn job reports, the Data Science industry is expected to grow from 37.9 billion USD in 2019 to 230 billion USD by 2026. In fact, Data Scientist has already been regarded as the sexiest job of the 21st century by Harvard Business Review. Due to this, Data Science has become one of the hottest and trending topics among students and professionals who want to build a career in this field. However, learning a new discipline can be challenging and overwhelming sometimes, so to mitigate this, there is a need for a solid educational plan or learning roadmap. A learning roadmap can be defined as a strategic plan with various steps to achieve a desired objective or goal. This article intends to provide you with a learning roadmap for Data Scientists or plan to learn and master the Why Become a Data Scientist in India? Data Scientists are in demand worldwide and in industries, and India is not an exception. Based on a survey by Monster jobs, 96% of the companies in India are looking to hire professionals to fill Big Data Analy...

Python is the language of choice for most of the data science community. This article is a road map to learning Python for Data Science. It’s suitable for starting data scientists and for those already there who want to learn more about using Python for data science. We’ll fly by all the essential elements data scientists use while providing links to more thorough explanations. This way, you can skip the stuff you already know and dive right into what you don’t know. Along the way, I’ll guide you to the essential Python packages used by the data science community. I recommend you bookmark this page to return to it easily. And last but not least: this page is a continuous work in progress. I’ll be adding content and links, and I’d love to get your feedback too. So if you find something you think belongs here along your journey, don’t hesitate to Table of Contents • 1 What is Data Science? • 2 Learn Python • 3 Learn the command-line • 4 A Data Science Working environment • 5 Reading data • 6 Crunching data • 7 Visualization • 8 Keep learning What is Data Science? Before we start, though, I’d like to describe what I see as data science more formally. While I assume you have a general idea of what data science is, it’s still a good idea to define it more specifically. It’ll also help us define a clear learning path. As you may know, giving a single, all-encompassing definition of a data scientist is hard. If we ask ten people, I’m sure it will result in at least eleven definit...