This Data Analytics course introduces beginners to the fundamental concepts of data analytics through real-world case studies and examples. You’ll learn about project lifecycles, the difference between data analytics, data science, and machine learning; building an analytics framework, and using analytics tools to draw business insights.
The global data analytics market is expected to expand at a CAGR of 30 percent from 2017-2023 and reach the market valuation of $77.64 billion by the end of 2023. Skilled professionals will be eligible for more than 90,000 available jobs in data analytics globally.
Upon successful completion of the Intro to Data Analytics for beginners course, Simplilearn will provide you with an industry-recognized course completion certificate which has lifelong validity.
To obtain the Introduction to Data Analytics course certification, you must:
Data analysts play a unique role among the many data-centric jobs often found in today's businesses. A Data Analyst works closely with identifying patterns and trends in data sets, working alongside organizations within the business or the management team to establish business needs, define new data collection and analysis processes, and add real value to a company. Simplilearn’s Data Analyst Master’s program will give you sufficient insights into data analytics tools and methodologies to excel in your next role as a Data Analyst.
Data analytics is one of the major areas to explore in this digitalized world. To learn data analytics, you need to have an in-depth understanding of SQL, Python or R programming, data visualization techniques, statistics, regression, and more. While you can get started on your own, it is recommended to take this training program and learn data analytics from industry experts. You’ll get up-to-date knowledge of the field and know about the strategies and best practices that are actually followed by companies.
Data analysis is a process of inspecting, cleaning, transforming and modeling data to discover useful information and support decision making to achieve business goals. There are various data analysis qualitative and quantitative methods and analytical or statistical tools used to extract the useful information and translate them into insights to make better business decisions,most of which are covered in this program.
Data analysis is the process of cleaning, transforming, and modeling data to find meaningful insights and make better decisions. Data analytics is a broad term that involves many diverse types of data analysis.
There are four different types of data analytics:
Most data analysts' job positions require a candidate to hold at least a bachelor’s degree in computer science, mathematics, statistics, or related field. It is, however, recommended to achieve a master’s degree in big data or data science or complete an online data analytics training course to enhance your credibility.
R, SAS, Python, and Apache Spark with Scala are the predominant languages used by Data Scientists and Data Analysts. Tableau, Excel, and QlikView are the most popular and effective analytics tools.
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