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Data Science Vs Machine Learning Vs Data Analytics

Terms like u2018Data Scienceu2019, u2018Machine Learningu2019, and u2018Data Analyticsu2019 are so infused and embedded in almost every dimension of lifestyle that imagining a day without these smart technologies is next to impossible. However, quite often it is witnessed that beginners get confused over similar terms being used interchangeably, like u2018Data Scienceu2019 and u2018Data Analyticsu2019. This PPT gives you a clear idea about why should you choose a particular Data field and what are career prospects in that domain.<br>Read the detailed blog here: <br>https://blog.simpliv.com/data-science-vs-machine-learning-vs-data-analytics/

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Data Science Vs Machine Learning Vs Data Analytics

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  1. www.simpliv.com

  2. What is Data Science? “Data Science is a field of technology that deals with exploring, modeling, and analyzing the big data to get meaningful insights from them that can solve a crucial business problem” www.simpliv.com

  3. Data Scientist Major Responsibilities • Feature Engineering:To prepare the proper input dataset that is compatible with the requirements of Machine Learning Algorithm. • Predictive Modeling: To predict the outcomes with the help of data models. These models are used for predicting various activities, events, phenomenon, etc. • Machine Learning and Deep Learning:Machine Learning seeks to educate the machines without human intervention. Deep Learning deals with artificial neural network which is nothing but multiple layers of algorithms. www.simpliv.com

  4. Skills Required to Become a Data Scientist • Common Data Science Skills with Data Analytics: Can be divided into Technicaland Non-Technical Data Science Skills. • Common Data Science Skills (Technical):Programming, Data Visualization • Common Data Science Skills (Non-Technical): Presentation & Communication Skills, Business Thinking • Data Science and Data Analytics are quite similar in broader perspective. • Unique Data Science Skills:Can be divided into Technical and Non-Technical Data Science Skills. • Unique Data Science Skills (Technical):Database, Statistics & Mathematics • Unique Data Science Skills (Non-Technical): Problem-solving, Data-driven Decision-Making www.simpliv.com

  5. Qualification Required to Become a Data Scientist • Bachelor's Degree in Computer Science, IT, or Related Field: Earning a Bachelor’s Degree in CS, IT or any related field will make the journey a little easier. • Master's Degree in Computer Science or Related Field:Earning a Master’s Degree in Data Science will introduce a candidate to the high-level programming and integration. • Experience in Related Field:Starting-off as an entry-level Data Analyst, or Data engineer, or Business Analyst will help you gain domain acumen, as well as technical expertise. READ MORE www.simpliv.com

  6. What is Machine Learning? “Machine learning is a field that deals with educating the machines to make them intelligent.” www.simpliv.com

  7. Machine Learning Expert Major Responsibilities • Machine Learning Experiments: To undertake various experiments and tests and run them. Fine tune the test results and implement them. • Train and Retain the System: To develop models that are capable of learning continually from a stream a data. • Perform Statistical Analysis:To select the appropriate datasets and data representation methods to run statistical analysis and fine-tune the test results. • Extend ML Frameworks:To work towards extending the existing ML libraries and frameworks. www.simpliv.com

  8. Skills Required to Become a Machine Learning Expert • Common Machine Learning Skills with Data Science: Can be divided into Technical and Non-Technical Data Science Skills. • Common Machine Learning Skills (Technical):Programming, Statistics & Mathematics • Common Machine Learning Skills (Non-Technical): Presentation & Communication Skills • Machine Learningis quite different from Data Science. However, there are some shared attributes between these two domains. • Unique Machine Learning Skills:Can be divided into Technical and Non-Technical Data Science Skills. • Unique Machine Learning Skills (Technical):Software Designing, Machine Learning Algorithm, Computer Science, Data Visualization • Unique Machine Learning (Non-Technical): Working in Teams, Time Management, Leadership www.simpliv.com

  9. Qualification Required to become a Data Analyst • Earn Qualification: • A Data Analyst needs to acquire either a Bachelor’s Degree in • Business related fields. • Gain Work Experience: • A Data Analyst needs to acquire either a Bachelor’s Degree in • Business related fields. READ MORE www.simpliv.com

  10. Data Science vs Data Analytics Which One to Choose? www.simpliv.com

  11. Data Science Vs Machine Learning Vs Data Analytics www.simpliv.com

  12. Conclusion Modern technology field is blurring the industrial boundaries, thanks to the explosion of data. As the dependence on data growing immensely, the need for having distinctive fields for separate uses has become imperative. Data Science, Machine Learning, and Data Analytics are three such fields, which have confused aspirants to a great extent. These slides will give you a clear picture about the three fields and why should you choose either of them. Click here to read the blog; www.simpliv.com

  13. Start Your Certification Journey with • SIMPLIV NOW! • Visit at • www.simpliv.com • toLEARN MORE! For queries: USA: +510 849 6155 Explore Blogs: blog.simpliv.com

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