{"id":265505,"date":"2024-08-01T13:54:09","date_gmt":"2024-08-01T13:54:09","guid":{"rendered":"https:\/\/imarticus.org\/blog\/?p=265505"},"modified":"2024-08-05T10:34:51","modified_gmt":"2024-08-05T10:34:51","slug":"data-science-roadmap","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/data-science-roadmap\/","title":{"rendered":"Data Science Roadmap: A Comprehensive Guide"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Data science, in recent years, has become one of the most popular fields of study in the globe. With the exponential growth of data, the demand for data scientists is expanding across industries. As per the report by the US Bureau of Labor Statistics, <\/span><b>data scientist jobs<\/b><span style=\"font-weight: 400;\"> are projected to have 36 per cent growth between the years 2021 and 2031. Therefore, aspiring IT professionals, who want a reliable career, should consider data science as their main area of study. However, it could be challenging to learn a new field. Hence, creating and applying a solid roadmap can help mitigate this hassle. So, let\u2019s start with our <\/span><b>data science roadmap<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><b>What is Data Science?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Data science is a multidisciplinary field of study that uses scientific methods, processes, systems and algorithms to extract insights and knowledge from structured and unstructured data. It incorporates several disciplines, such as statistics, data analysis, machine learning and visualisation to discover hidden patterns, trends and correlations in data. Data science plays a vital role in decision-making, strategic planning and problem-solving across companies, driving a revolution and aiding organisations in making data-centric decisions.<\/span><\/p>\n<h2><b>Data Science Roadmap<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">This <\/span><b>data science roadmap <\/b><span style=\"font-weight: 400;\">provides an organised path to learning the important concepts and skills required for success in the field of data science. So, let\u2019s dive into this!<\/span><\/p>\n<p><b>Mathematics: <\/b><span style=\"font-weight: 400;\">Math skills are required to understand several machine-learning algorithms that are crucial in data science. These include arithmetic, algebra and geometry. Additionally, learning mathematical notation and symbols, commonly used in data science, is important. So, learn the following mathematical concepts to start your data science journey.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Linear Algebra<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Matrix<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Calculus<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimisation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Probability Theory<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\"><b>Statistics:<\/b><span style=\"font-weight: 400;\"> It is essential to understand statistics as this is a part of data analysis and helps you collect, analyse, interpret and present data. It is a key element of data science as it enables us to draw significant insights from data and make well-informed decisions. So, the following are a few concepts that you must learn:<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Basics of Statistics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hypotheses Testing<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sampling Distribution<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Regression Analysis<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Correlation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Computer Simulation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Basics of Graphs<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Programming Skills: <\/b><span style=\"font-weight: 400;\">Programming skills are crucial for data scientists to analyse, employ and visualise data. So, developing programming skills with an emphasis on data science is important. Also, learning programming languages, such as Python, r, Java, Scale and C+, is useful for better performance.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\"><b>Python:<\/b><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Basics of Python<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Numpy<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pandas<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Matplotlib\/Seaborn, etc.<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>R:\u00a0<\/b><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">R Basics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">dplyr<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ggplot2<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tidyr<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Shiny, etc.<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>DataBase:<\/b><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SQL<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/MongoDB\"><strong>MongoDB<\/strong><\/a><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>Other:<\/b><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Structure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Web Scraping (Python | R)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Linux<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Git<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\"><b>Machine Learning (ML): <\/b><span style=\"font-weight: 400;\">Machine learning is among the most crucial parts of data science. So, it is important for data scientists to understand the basic algorithms of Supervised and Unsupervised Learning. Various libraries are available in Python and R for applying these algorithms.<\/span><\/li>\n<\/ul>\n<p><b><\/b><br \/>\n<b><\/b><\/p>\n<ul>\n<li aria-level=\"1\"><b>Introduction:<\/b><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How Model Works<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Basic Data Exploration<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">First ML Model<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model Validation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Underfitting &amp; Overfitting<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Random Forests (Python | R)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">scikit-learn<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><b>Intermediate:<\/b><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handling Missing Values<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handling Categorical Variables<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pipelines<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cross-Validation (R)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">XGBoost (Python | R)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Leakage<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li aria-level=\"1\"><b>Deep Learning: <\/b><span style=\"font-weight: 400;\">TensorFlow and Keras are used in deep learning to develop and train neural networks for structured data.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">TensorFlow<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Keras<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Artificial Neural Network<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Convolutional Neural Network<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Recurrent Neural Network<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A Single Neuron<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Deep Neural Network<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Stochastic Gradient Descent<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Overfitting and Underfitting<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dropout Batch Normalization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Binary Classification<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Natural Language Processing (NLP): <\/b><span style=\"font-weight: 400;\"><a href=\"https:\/\/imarticus.org\/blog\/introduction-to-natural-language-processing\/\"><strong>Natural Language Processing<\/strong><\/a> (NLP) is a type of machine learning technology that allows computers to understand and operate human language. In NLP, you need to learn to work with text data.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Text Classification<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Word Vectors<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Feature Engineering: <\/b><span style=\"font-weight: 400;\">In Feature Engineering, you need to learn techniques to discover the most effective way to improve your models.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Baseline Model<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Categorical Encodings<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature Generation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature Selection<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Data Visualization Tools: <\/b><span style=\"font-weight: 400;\">Learn to create great data visualisations. It is an excellent way to see the power of coding.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Excel VBA<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">BI (Business Intelligence):<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tableau<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Power BI<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Qlik View<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Qlik Sense<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Deployment: <\/b><span style=\"font-weight: 400;\">Whether you are fresher or have over 5 years of experience or 10 years of experience, deployment is an important element for data science. Because it will definitely provide you with the fact that you worked so much.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Microsoft Azure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Heroku<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google Cloud Platform<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Flask<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">DJango<\/span><\/li>\n<\/ul>\n<p><b>\u00a0\u00a0<\/b><\/p>\n<p><b>Other Points to Learn: <\/b><span style=\"font-weight: 400;\">There are some other points that you must learn as a part of your data science journey. They include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Domain Knowledge<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Communication Skill<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Reinforcement Learning<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Different Case Studies<\/span><\/li>\n<\/ul>\n<p><b>How to Become a Data Scientist?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">To become a successful data scientist, you need to follow the following steps:<\/span><\/p>\n<ul>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Get a bachelor\u2019s degree in the field of data science\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learn programming skills required<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Enhance your related skills<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Get a data science certification<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Do internships as they are a great way to learn practical skills the job demands<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Master in data science tools<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Start your career in data science.<\/span><\/li>\n<\/ul>\n<h2><b>Data Scientist Salary in India<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">The average salary for a data scientist is Rs. 7,08,012 annually. Freshers can start their careers with a salary of around Rs. 5,77,893, while experienced professionals can expect about Rs. 19,44,566.<\/span><\/p>\n<h4><b>Conclusion<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">The demand for data scientists is growing, offering impressive salaries and great work opportunities. The <\/span><b>data science roadmap<\/b><span style=\"font-weight: 400;\"> includes important areas, including mathematics, programming skills, machine learning, deep learning, natural language processing, data visualisation tools and deployment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Want to transform your career in data science? Then, enrol in a <\/span><span style=\"font-weight: 400;\"><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\"><strong>data science course<\/strong><\/a> &#8211; Postgraduate Program in Data Science and Analytics<\/span><span style=\"font-weight: 400;\"> offered by Imarticus Learning. This program is suitable for graduates and IT professionals who want to enhance a successful data science and analytics career.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data science, in recent years, has become one of the most popular fields of study in the globe. With the exponential growth of data, the demand for data scientists is expanding across industries. As per the report by the US Bureau of Labor Statistics, data scientist jobs are projected to have 36 per cent growth [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":265534,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[],"class_list":["post-265505","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/265505","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=265505"}],"version-history":[{"count":1,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/265505\/revisions"}],"predecessor-version":[{"id":265506,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/265505\/revisions\/265506"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/265534"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=265505"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=265505"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=265505"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}