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Data Science Developer <br />Program length: 160 hours (4 hours a day, 4 days a week for 10 weeks) <br />Instruction method: Distance <br />Program Description: In this Certificate course, participants will learn the basics of Data Science. Data <br />science is a multidisciplinary field. It encompasses a wide range of topics including: Understanding of the <br />data science field and the type of analysis carried out, Mathematics, Statistics, Python, Applying advanced <br />statistical techniques in Python, Data Visualization and Machine Learning. <br />Learning Objectives: <br />After completing this course, students will learn Advanced Excel, Python, JavaScript, HTML/CSS, API <br />Interactions, Social Media Mining, SQL, Tableau, Advanced Statistics, Machine Learning, and R. <br />Occupational Objectives: Successful graduates will be employable as Data Analysis professionals. (SOC <br />Code 15-2051) <br />Detailed Syllabus: <br />Advanced Excel for Data Analysis: (32 hours) Learn to do more with Microsoft Excel. In this module, <br />we'll cover advanced topics like statistical modeling, forecasting and prediction, pivot tables, and VBA <br />scripting. You'll even learn to model historic stock trends. <br />Python for Data Analytics: (32 hours) Gain a solid foothold in one of today's fundamental programming <br />languages. You'll develop proficiency in core Python; data analytic tools like NumPy, Pandas, and <br />Matplotlib; and specific libraries for interacting with web data, like Requests and BeautifulSoup <br />Databases: (32 hours) Dive deep into the most prolific database languages: SQL and NoSQL. Work with <br />MySQL and MongoDB to organize data into well -structured and easily retrievable data formats. Work on <br />a case study to combine data from different sources into one database. <br />Data Visualization: (32 hours) Building visualizations is of little benefit without a way to communicate <br />the message. In this module, you'll learn how to use the core web development technologies (HTML, <br />CSS, and JavaScript) to create new and interactive data visualizations that you can share with everyone on <br />the web. <br />Machine Learning: (32 hours) First, you will be learning about the purpose of Machine Learning and <br />where it applies to the real world. Second, you will get a general overview of Machine Learning topics <br />such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. <br />Updated 091323 81 <br />