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Courses In Analytics

With the influx of data in the 21st century, the demand for business professionals who can collect and analyse this data has increased manifold. According to a 2016 report by McKinsey and Company, the data doubles in volume every three years. There is a shortage of 1.5 million managers skilled enough to use this data to make sound business decisions. Knowledge of business analytics helps to turn data into actionable intelligence, thus making it quite crucial to the success of any organisation. Business Analysts use a range of statistical and quantitative methods, tools and predictive models to gain insights and make data-driven decisions.

courses in analytics


Here is our list of top-ten picks for best online courses and specialization programs that will help you understand the fundamentals of business analytics and equip you with necessary knowhow about right tools and techniques. These will open the gateway of better opportunities for all learners. This list of Best Business Analytics Courses, Tutorials and Certifications available online includes both free and paid resources. While most of these can be taken up by complete beginners, a few are advanced level courses that require some prior knowledge.

The course begins with introduction to best practices for using data analytics, identifying key metrics etc. It also covers data analysis techniques used by giants like Amazon, Airbnb and Uber and how these techniques help them in their respective industry. Then it dives into Excel, Tableau, and MySQL, followed by practices to translate and persuasively communicate insights into actionable recommendations for decision-makers. This course can be taken by beginners with no prior knowledge of business analytics.

This course has been designed for learners who are new to the field of data and analytics. So, no prior knowledge or background in business analytics and statistics is required. You will need PowerPivot and MS Excel to complete some of the exercises in the course.

This Certification program on Business Analytics Fundamentals aims to provide principles and practical applications of business analytics. It imparts foundational and practical skills and knowledge to analyse and understand data, present your findings effectively and draw meaningful conclusions about data. It covers latest modelling techniques and advanced Excel functions.

The aim of this course is to help you make better business decisions using analytic methods. You will learn to reframe a business question as an analytics question, to decide what data you need and where to obtain it, and to prepare that data for analysis. You will practice how to create compelling data visualizations and learn about the tools to achieve that. You will also understand how to infer and present insights from the analysis. Thus this course teaches the complete end-to-end process of making effective data-driven and evidence-based decisions.

This MicroMasters program in Business Analytics is a professional and academic credential offered by Columbia University on edX platform for online learners. It is a series of 4 graduate level courses that empower learners with the skills, insights and understanding to improve business performance using data, statistical and quantitative analysis, and explanatory and predictive modelling to help make actionable decisions.

This Specialization is designed for students and professionals interested in practical applications of business analytics techniques and big data. It covers a wide variety of analytics approaches in different industry domains including media, communications, public service etc.

Learners who complete this specialization will be fully accomplished experts in big data management, with a robust understanding of how data can be used to leverage strategic value. They will be able to combine and manipulate data sets, and interpret them. The courses also prepare them to present their interpretations and value case to potential stakeholders successfully.

This is an advanced level program and requires a good statistical background. It is suggested that learners have knowledge of R programming language and analytics to complete the coursework. Some experience with SQL and machine learning will be useful too.

Code Spaces is a platform for learners to find the best courses, certifications and tutorials on the web. Our team of experts handpicks these resources based on several parameters and brings to you the best recommendations that you can use to learn a new skill or upgrade your existing knowledge. These resources include both free and paid ones created by top professionals, schools and companies. Explore, Learn and Master anything with us.

Students have the flexibility to pursue the Master of Science in Analytics degree on a part- or full-time schedule. Part-time students enroll in two courses each quarter and take their courses in the evenings or on Saturdays. Full-time students take three courses per quarter. Some of their courses may be offered during the day. All courses are taught at the NBC Tower in downtown Chicago.

Upon acceptance into the program, students will have an opportunity to complete Foundational Skill Assessments and earn an exemption for noncredit courses in the areas of statistics, advanced linear algebra, R, and Python. Assessment scores of 80% or higher will result in the relevant Foundational course being waived. Assessment scores of less than 80% will require the completion of the corresponding Foundational course.

The four foundational courses are listed below. Please note that the Introduction to Statistical Concepts and R Course are considered pre-quarter courses and therefore take place during the 5 weeks leading up to your first quarter with the program.

This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program. The course covers theoretical distributions and the way these distributions are used to assign probabilities to events in some depth. The course also introduces students to descriptive statistical methods to explore and summarize data, methodologies for sampling units for measurement or analysis, drawing inferences on the basis of knowledge gained from samples to populations, assessing relationships between variables, and making predictions based upon relationships between variables.

If you are uncertain of your preparation in these foundational topics, we have identified four Coursera courses which cover very similar topics. You can review the Coursera curricula to see if you are already well-prepared, or if you like, study their materials to brush up on some or all of these topics.

Core courses allow students to build their theoretical analytics knowledge and practice applying this theory to examine business problems. Each of the seven core courses is required to earn the Master of Science in Analytics. Students choose either Leadership Skills: Teams, Strategies, and Communications (MSCA 31003) or Data Science for Consulting (MSCA 31015) as one of the core courses.

Course emphasizes applications of these models to various fields and covers main steps of building analytics from visualizing data and building intuition about their structure and patterns to selecting appropriate statistical method to interpretation of the results and building analytical model. Topics are illustrated by data analysis projects using R. Familiarity with R at some basic level is not a requirement but recommendation. Students can pick up the programming language by following the descriptions of the examples.

Students will learn the big data infrastructure including Linux, massive parallelization, and distributed computing, and how to apply both Hadoop and Spark map-reduce concepts for clustering, similarity search, web analytics and classification. During the course, we will cover the applications of NoSQL systems, such as JSON stores, object storage and Elasticsearch. The cloud computing section of the course will focus on virtualization and container orchestration, including virtual machines, dockers and Kubernetes. During the course students will gain hands-on expertise leveraging Hive, Pig, Python and PySpark for big data applications in client-server environment.

In Leadership Skills: Teams, Strategies, and Communications, students learn how to work effectively in teams to identify, structure, and communicate the business value of data analytics to an organization.

At the end of the course, students should have the ability to describe business problems that lend themselves to a data analytics approach, position these problems from the perspective of a coherent business strategy, and represent the power of analytics to a business audience. Students should also understand how to harness the powerful dynamics of a team to achieve excellence in the world of data analytics.

The demand for analytics and data-driven decision making creates a market demand for expertise driven leadership - evidenced in knowledgeable consultants that bring data science and results-driven impact to clients.

Explore advanced analytics strategies and applications. Students in the 12-course curriculum are required to complete three elective courses. Our program continually adds electives to evolve with the analytics landscape. Alumni are able to take classes, when available, at reduced tuition.

This course concentrates on the following topics: review of financial markets and assets traded on them; main characteristics of financial analytics: returns, yields, volatility; review of stochastic models of market price and their statistical representations; concept of arbitrage, elements of arbitrage pricing approach; principles of volatility analyses, implied vs. realized volatility; correlation, cointegration and other relationships between various financial assets; market risk analytics and management of portfolios of financial assets.

The course puts special emphasis on covering main steps of building analytics from visualizing data and building intuition about their structure and patterns to selecting appropriate statistical method to interpretation of the results and building analytical models. Topics are illustrated by data analysis projects using R. 041b061a72


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