{"id":50757,"date":"2018-04-11T14:01:18","date_gmt":"2018-04-11T08:31:18","guid":{"rendered":"https:\/\/staging-imarticus.kinsta.cloud\/?p=50757"},"modified":"2022-12-15T08:48:49","modified_gmt":"2022-12-15T08:48:49","slug":"what-are-data-science-courses","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/what-are-data-science-courses\/","title":{"rendered":"What Are Data Science Courses?"},"content":{"rendered":"

Data is the very life breath of data analytics and is being generated by the moment. Though it sounds easy, handling data can run into a variety of complex issues during its cleaning and formatting, preparation and merger, and the final inferential steps of gainfully using data. One does not need a technical degree to choose this lucrative field to further your career. However, it is a must that you do a Data science Course<\/a><\/strong> from a reputed institute like Imarticus to get the technical expertise and certification which is a validation of being job prepared. You will need an eclectic mix of personal traits, technologically sound knowledge of programming languages and a data science tutorial to set you on the right track.
\nLet us learn a little more about where data analytics and trend spotting led to success stories in a big way. The CIOs at IDG connect recently shared these examples to learn from and motivate others with.<\/p>\n

Data Science Success Stories<\/strong><\/h2>\n

\u2022 ARC tackled change and new data-management using trending technology.
\n\u2022 Shell uses data analytics to predict when and where their oil-rigs and machines could fail through constant monitoring and predictive analysis.
\n\u2022 Shrimp farmers have a better life thanks to Cargill\u2019s analytics.
\n\u2022 Dr Pepper Snapple Group used algorithms and machine learning to grow.
\n\u2022 TD Bank created lakes of data and used the foresight effectively for growth.
\n\u2022 The disrupt at Bechtel enabled the setting up of its Big Data Center of Excellence.
\n\u2022 Merck also used its analytics and data optimally while Monsanto used an ML algorithm to help optimal planting.
\n\u2022 The ML algorithm of RRD helped it identify new business opportunities.
\n\u2022 Predictive Analysis was the foundation of Pitt Ohio\u2019s success.
\nThe data scientist is one of the most sought-after trained experts who is paid very handsomely and never suffers from want of job demand says the business review by Harvard. A data scientist is an all-in-one person who is the go-to person for data analytics and is a strange mix of computer and databases scientist, math expert and trend spotter with the technical expertise to handle large volumes of data from different sources, clean it, and draw complex inferences from it.<\/p>\n

Step-by-Step Data Science Tutorial\u00a0<\/strong><\/h2>\n

A step-by-step data science tutorial to succeed in a data science career<\/a> is detailed below. The best way to achieve all that is laid out in the tutorial is to do a data science course.
\n\u2022 Do a Data science Course to learn about the data sciences foundational concepts like ML, statistics, algebra, calculus etc. A data scientist needs more than computer science and programming skills because he has to visualize the data, spot trends, and make inferences from them.
\n\u2022 Learn about the types of data and databases as this forms the foundation and lies at the very heart of your career. More the better!
\n\u2022 Learn coding which forms the building pillars for your career. You need to have proficiency, knowledge and be adept at coding efficiently for success in this career.
\n\u2022 Practice coding and build your projects with the real-time practice of your coding skills. The more mistakes you make in the beginning the better you get at coding and programming with it.
\n\u2022 Use bigger databases and compatible software to practice on. Hadoop, MapReduce and such can help you deal with segregated and non-labelled data too. Data from various sources, merging, cleaning and preparing your data is of utmost importance.
\n\u2022 Get proficiency in data-munging which means transforming the data into a well-analyzed format which is easy to read, study and analyze further.
\n\u2022 Don\u2019t stop learning ever. The evolving fields of data and data analytics need constant upgrades, tweaks and techniques to handle them. Curiosity and never-ending learning from the peer group will make an ace of you.
\n\u2022 Develop personal presentation traits and effective communication skills. These are needed to infer, justify based on data and provide the results in the form of gainful insights. Feedback and constructive criticism should always be welcome. They improve your skills.
\n\u2022 Get on GitHub and participate in hackathons like Kaggle, DataHack, etc. Also, update your portfolio with your projects undertaken.
\n\u2022 Look for project opportunities and suitable openings to display your data science tutorial portfolio of projects and get ahead of the others.
\nThe most popular way to achieve your goal is to enrol in a Data science Course. Do so with a reputed institute like Imarticus who have years of industrial experience in training data scientists and analysts with the nest courses. One of the huge advantages of doing the course with them is that you get sufficient hands-on experience, a globally accepted curriculum and excellent learning support in the form of certified trainers, ample projects in real-time industry-relevant projects, industry mentors and an excellent peer group.<\/p>\n

Course learnings:<\/strong><\/h4>\n

The following modules will be covered comprehensively in most courses and form essential skills for a data engineer or scientist.
\n\u2022 Basic concepts and data-management fundamentals.
\n\u2022 Algorithms that involve complex mathematical fields like differentiation, calculus, linear algebra and others.
\n\u2022 Statistical and data modelling.
\n\u2022 Concepts and methodology of Data Sciences.
\n\u2022 Data cleaning, scraping and mining.
\n\u2022 Data Analysis including logistic and linear regression, decision trees and such concepts from statistical analysis.
\n\u2022 Machine Learning.
\n\u2022 Data Engineering
\n\u2022 Artificial Intelligence.
\n\u2022 Programming languages like Python, SAS, R, SPSS, Stata, Minitab, Matlab and such.
\nRelax you only need working knowledge to get by and help you understand the underlying concepts, visualize your data and then apply the appropriate tools to get the right inference and especially when it comes to interpretations and predictions.
\nConcluding Note:<\/strong>
\nGathering and possessing a data scientist\u2019s skills is no mean task to achieve. It gets better with experience and so does the payouts. The job interview is one area where you need skills not taught formally in any course and is totally dependent on yourself and how you stand out from the competition using your technical and communication skills. The data science tutorial course at Imarticus and their certification are coveted and a great validation of your resume, knowledge of practical implementations of learnt skills. You just need dedication and perseverance to settle down in this high paying career.<\/p>\n

\n
\n
\n
\n
\n

We offer data science course at our centers in\u00a0Mumbai, Thane<\/a>, Pune, Jaipur, Delhi<\/a>, Gurgaon, Bangalore<\/a>, Chennai, Hyderabad<\/a> and Coimbatore.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Data is the very life breath of data analytics and is being generated by the moment. Though it sounds easy, handling data can run into a variety of complex issues during its cleaning and formatting, preparation and merger, and the final inferential steps of gainfully using data. One does not need a technical degree to […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"no","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[203],"class_list":["post-50757","post","type-post","status-publish","format-standard","hentry","category-analytics","tag-data-science"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/50757","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=50757"}],"version-history":[{"count":0,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/50757\/revisions"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=50757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=50757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=50757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}