Data Analysis

If you are looking for free data analysis courses, then the Stanford Statistics course, the Analyzing and Visualizing Data the Google Way course, the Data Science Math Skills course, the Statistical Inferences course, and the Python Statistics for Financial Analysis course are all great options. These courses offer the chance to gain a comprehensive knowledge of the latest data analysis techniques, from the fundamentals to the more advanced topics.‎

If you're looking to begin a career in data analysis, the best courses to start with are Introduction to Data Analytics, Stanford Statistics, Data Analysis with Python, Analyzing and Visualizing Data with the Google Way, and Excel Basics: Data Analysis with IBM. These courses teach the fundamentals of data analysis, and offer comprehensive and in-depth introductions to the field.‎

Those interested in advanced data analysis should explore the IBM AI Workflow for Data Analysis and Hypothesis Testing from Coursera. Alongside it, take a look at the course on Machine Learning Data Lifecycle in Production, Advanced Valuation and Strategy, Automated Machine Learning for Datasets and ML Models, and Probabilistic Graphical Models. Each of these courses covers different aspects of advanced data analysis and will help equip the enrollee with the skills necessary to do the job.‎

Data analysis is the process of applying statistical analysis and logical techniques to extract information from data. When carried out carefully and systematically, the results of data analysis can be an invaluable complement to qualitative research in producing actionable insights for decision-making.

If that sounds a lot like data science, you’re right! It’s a closely related field, but there are important differences. Data scientists typically come from computer science and programming backgrounds and rely on coding skills to build algorithms and analytic models to automate the processing of data at scale. Data analysts typically have backgrounds in mathematics and statistics, and frequently apply these analytic techniques to answer specific business problems - for example, a financial analyst at an investment bank.‎

Data analysts don’t do as much coding as data scientists, but it’s still important to know your way around certain programming languages. In particular, SQL (Structured Query Language) is the industry standard for navigating large databases, and statistical programming languages like R or Python are essential for performing advanced analyses on this data.

Data analysts also rely on more typical business programs. While Microsoft Excel isn’t as powerful as SQL, R, and Python, it can get the job done when working with relatively smaller datasets, and may be the best (and cheapest) tool for the job for early-stage lean startups. Data visualization and presentation skills are also a huge part of the job, which typically requires learning new programs like Tableau as well as mastery of standard business software like Excel and Powerpoint. For more details, read this article about in-demand data analyst skills to get hired. In addition, read this article about how to become a data analyst with or without a college degree.‎

According to Glassdoor, the average median annual  salary for a data analyst was $69,291 as of November 2019. Of course, because data analysis is in demand across a wide range of industries, the salaries of two data scientists with similar job descriptions might be quite different depending on whether they're working with a small startup or a global hedge fund. Around this median, Glassdoor found data analyst salaries as high as $105,000 and as low as $48,000.‎

As with data science, online courses are a great way to learn data analysis skills, and Coursera offers Professional Certificates, MasterTrack certificates, Specializations, Guided Projects, and courses in data analysis from top universities like Duke University, University of Michigan and companies like IBM and pwc.

Many courses are shared between data science and data analysis, such as introductions to data science and data science programming with SQL, Python, and R. There are also courses more specific to data analysis, such as data analysis and interpretation skills, Excel skills for business, exploratory data analysis, and a variety of courses on statistics and probability.‎

As you gain experience as a data analyst, you may encounter opportunities to advance your career in a few different directions. Depending on your goals and interests, you may progress into data science, management, consulting, or a more specialized data role. Read more about the data analyst career paths and salaries in this article.‎

People with strong math and statistical skills are best suited for roles in data analysis. A data analyst is responsible for collecting data and performing statistical analyses on a large dataset, so it’s important that people in data analysis roles are organized, detail-oriented, and able to work smart on tight deadlines. In addition to high-level math skills, a data analyst should be familiar with various programming languages and have the ability to analyze and summarize datasets.‎

Many data analysts work on Wall Street or with hedge funds to help investors and big banks make financial decisions for their portfolios and clients. These data analysts are responsible for collecting and analyzing huge amounts of financial data for colleagues and clients. Common career paths for someone in data analysis also include working in health care or insurance companies.‎

It’s important for anyone studying data analysis to have strong math skills, so learners may consider topics that cover inferential statistics, probability and data, and data science for math skills. A data analyst also needs to be familiar with computer programming, so topics that examine applied data science with Python are a must. For learners interested in how to do data analysis in a team setting, topics in managing data analysis and building a data science team may help you realize your team’s potential and offer managing and planning tips.‎

An investment bank is the most common place that hires someone with a background in data analysis. Financial institutions often hire data analysts for a management or leadership track. The government hires people with a background in data analysis to collect and interpret data. Health care companies including insurance companies hire data analysts to manage data from insurance companies, billing claims, and patient satisfaction surveys.‎