Data Analysts use raw form of data or information, present in numbers or qualitative shape, to devise a pattern and then tell an understandable story for business decision-makers to plan their next steps of action. It is mostly storytelling that can be easily understood by decision-making individuals to make informed decisions based on that story. The story however is based on actual data, which helps identify the loopholes or opportunities that can be taken to the business’s advantage. In simpler words, the primary goal of a data analyst is to work with large volumes of absolute raw but complex data, produce actionable insights to help their company solve problems.

A data analyst possesses analytical skills that pertain to various analytical tools, software and, programming languages. It may include experts to be hands-on with, but not limited to, SQL, Microsoft Excel, Python, R programming, critical thinking, an eye for detail, and presentable reporting. After identifying the insights, these professionals provide a big picture that helps make improvements in key business functions, for example, marketing, sales, consumer relationship strategies, product strategy, and much more.

These critical business functions are self-sufficient as far as the actual fieldwork is concerned, but when it comes to strategy and how their fieldwork is turning out to be for the business, that is when data analysts come in to play. They guide the teams about how their performances are reflecting based on the data they receive, inform what is really going on, and then guide them strategy-wise, so they can tailor their work according to the need. The goal is the same for all, higher returns in terms of performance and financial numbers.

Data analysts have this exciting job of identifying meaningful insights by playing with numbers and charts, but they can’t do it alone. What they need to carry this valuable task in an accurate and efficient way is using a bunch of technical tools. As mentioned above, they have at their disposal various statistical equations, software that is specialized, and multiple coding languages.

It is impossible however to keep numerous SQL commands and Excel formulas on your fingertips at any given time. Whether you’re getting introduced to playing with raw data or have been a performing data analyst, it must be well understood that you don’t need to memorize all the formulas and equations, which might add up to a hundred of them, but only a handful that will best suit that particular situation. Asking the right questions might help you identify the best tools to work with. Being a data analyst, a library offering critical functions and formulas all in one place, life becomes a lot easier.

Here are some of the reference sheets that qualified for us as the most useful at-a-glance documents that will assist you to build tables, produce custom reports, and identify the workable margins in a more fast-paced manner. Saving this cheat sheet to your Favorites will save you a lot of time and effort. In a time-bound situation, it will help you pull out direly needed formulas much faster than putting your brain to exercise and leave things to chance.

Data Visualization

A data analyst can pull out signals from numerical findings in a very short instant, but to make it readable for others, it is important to illustrate process and results for the sake of communicating the impact of those findings and suitable actions to be taken. An analyst’s job includes building visually striking charts, aligning data, reflecting them in charts, and gather key numbers in a dashboard. These and some other tasks are a key pillar to building a long-term career around data.

Here are some of the analysts’ most favorable tools. Ggplot2 is one of them. This tool is often used for programming in R. With its own syntax and formulas, you will find a reference sheet exclusive to this tool and published by R Studio.

You will also find content that highlights the principles and guidelines for visualizing data clearly, and it is going to help provide a refresher to your memory.

Statistics & Probabilities

An analyst has an undetachable bong with Math. Of course, some of the statistical functions will be utilized on a very frequent basis but some will be used once a long while. Those in frequent use will be easily memorized. For those used less often, you shouldn’t have to dig out your old Stats 101 study guides to refresh your memory.

Below is a collection of material you might find extremely useful while juggling through various statistical formulas, equations, probabilities, terms, and calculations.

Microsoft Excel

Microsoft Excel doesn’t seem to have lost its charm, even though the last decade has produced so many advanced tools and programming languages. It is mainly because of the way data is presented, and then the features included that convert the data into various forms of presentable charts and tables. If you are an in-practice analyst, you’re probably already pretty adept with Excel basics. However, with the remarkable depth of this tool, there’s always a chance you don’t know a specific shortcut or a formula lengthy in nature, which you might want to just copy and paste to do your job.

Find below a complete guide to in-depth excel formulas, immensely useful shortcuts, statistical and logical functions, and tricks to manipulate data in order to craft patterns and draw a big picture to work on.

Google Analytics, Data Studio & Trends

Analysts often fall into the marketing department, helping other marketing professionals and sales personnel steer their way in the right direction. When that is the case, getting into a relationship with a Google tool is inevitable. With digitalization and shift of customers towards digital touchpoints, Google has been playing a massive role in understanding how consumers react to various marketing strategies and digital advertisement efforts. Thus, using Google’s highly sensitive marketing analytics tools is a must. Some of the strongest and most widely used tools include Google Analytics, Data Studio, and Trends. You may use Trends to identify hottest search topics, play with Data Studio to build reports, or study customers visiting your digital touchpoint, a website, in Analytics. The search giant is giving you tons of valuable information about how well your site is performing and how to reel in more customers.

Using Google tools on a regular basis would yield a great value if you can automate your reporting and dashboards. However, not everything can be automated, and some short-term projects are not worth the effort or time to automate reporting. In this particular situation, this list of guides will assist you to get all the important information from these three master tools by Google.

Enroll in our Data Analytics Bootcamp program and get trained for the latest skills, tools, and programming languages at QuickStart.com

SAS

The SAS programming language was developed for data management and analytics. It’s high on the list of languages you’ll need to know - especially if you eventually advance into machine learning and data science. It’s frequently used in business and academic settings to predict outcomes based on existing data patterns.

If SAS is just one of many programming languages you work with, it can be hard to remember the different rules and syntax used in each one. Good thing you don’t have to. Below are our top three SAS cheat sheets for data analysts.

R

The R programming language was developed specifically for statistical calculations. It’s used to organize data and create graphs. Data analysts often use R to import and clean data, and sometimes favor it over other languages since it lends itself to a wide variety of statistical computations.

If you need to get a little more fluent in R or troubleshoot a bug, below are the resources you need to make the most of this powerful language. We’ve compiled some cheat sheets for R and RStudio (the app for editing and executing R commands). We also covered dplyr and tidyr, two popular programs that many analysts use in conjunction with R.

SQL

SQL one of the top-most coding languages in terms of preference by statisticians and analysts. Professionals who are found to be experts in SQL have been considered masters of their trait. The tool is perfect for aligning tables with large datasets, allowing multiple other users to work with the same data without worrying about overwriting each other’s work.  

It is almost impossible to memorize the whole SQL library of keywords, commands, queries, and all things SQL, but you don’t have to worry about it. Below is a list where you can find commands and keywords, data types, queries, and a general SQL guide helping you keep your SQL memory always fresh.

Python

Who doesn’t know about Python? Data analysts to data scientists, web developers to application developers, Python is a favorite to various tech-based roles who use it to their advantage. The language isn’t just numbers and data, but in the world of programming, Python is considered to be one of the most versatile and languages in the programming world. Newly learned programmers find it easy to grab the concepts of Python, the reason for which is the structure and simple syntax of this language. The best thing is the versatility if offers along with being a simple-to-understand language. There are a thousand of applications and software tools developed using Python. For data analysts, scientists, and even financial analysts, Python is considered to be a must-have skill. The variety of options you can explore with Python is hardly possible with other languages, not denying the use of other options though.

As a data analyst, you’ll probably run into some lines of python now and then even if you’re not using it every day. Use the below reference sheets, dictionary, and style guide to quickly write and edit in python. We also included some cheat sheets for pandas, the popular software used in conjunction with python to manipulate tables.

 

 

If you have gone through the whitepaper till here, you might have found this as a great resource with a collection, meeting all your visualization, statistical, coding, and analytics needs. With this guide at your disposal, you can dig into any of the above and make your slightly rusted skills shiny again.

Data analysis is a field of information technology that keeps evolving, and so do the tools of this trade. Stay up-to-date on data news and development to keep your skills sharp and take advantage of all the latest technology. Let’s now talk some more about data analysis as a field to pursue. Below we have mentioned some job titles you can fit in with the data analytics skills and have further talked about the scope in terms of industries that are hiring data analysts.

Data Analytics Job Titles

Data is a critical asset in any industry because every industry serves customers. Whether it is business to business, business to consumer, or business to employee, a business cannot exist without customers. Data analysts gauge data from various sources, pertaining to a number of different scenarios, but each scenario will be analyzed to understand how better we can serve customers, how better we can market our products to consumers, and how better we can improve our service or offering to become more acceptable. In the next section, we will discuss all the hottest industries hiring data analysts, but for now, let’s see what job titles you can fit if you possess a decent set of data analytics skills.

  • Data Analyst
  • Business Analyst
  • Financial Analyst
  • Operations Analyst
  • Risk Analyst
  • Research Analyst
  • Data Journalist
  • Business Intelligence Analyst
  • Financial Analyst
  • Marketing Analyst

Whatever the title may be, the core task will always revolve around data and converting it into actionable business insights. Let’s go on and take a look at the industries that are most actively hiring data analysts.

Industries Hiring Data Analysts

Analytics is a powerful area for businesses to gain competitive advantage. Every business is different, every product somehow differs in nature, and so gaining traction may require a customized strategy. To identify, build, and then improve on that strategy, data is the backbone. Below is a lost of industries you can find opportunities most in number.

Consultant Firms in Finance, Sales & Business

Being the consultants, these firms need to know how the business is performing, what actions are reflecting success, and where do they need to be more careful. These firms hire data analysts to help them read the data for clients and present them action-steps that would turn out to be the success factors in the future.

Technology

Technology giants like Google, Amazon and Microsoft have proper departments who are responsible for analyzing huge chunks of data coming from consumers. Since these companies have users spread across the globe, and in an insane number, it is of utmost importance for them to hire talented data analysts who are not just savvy with the tools and skills, but also possess soft skills to beautify all the heavy lifting and present understandable facts to business owners. Not just the names mentioned above, there are plenty of other technology companies that cannot imagine functioning without people handling and effectively using data. Also, as a matter of fact, the most challenging industry to be a data analyst for is technology, since the changes in consumer preferences are rapid and consumer behavior is ever-so-volatile.

Marketing/Communication

Marketing is a function that feeds on consumer behavior. Identifying what your consumer wants at a certain time is the key to getting prepared for serving them in a tailored way. With tools like Google Analytics, it has become much easier to read patterns on the digital front, compared to earlier times when ATL marketing efforts could only be judged by the sales numbers after running a campaign in an area. This is by far the best era for data analysts to live in, as they have now got the power to dig in deep and make a mark in understanding customer preferences.

Insurance

Data analysts are vital for any type of insurance. May it be life insurance, car insurance, or home insurance, data analysts need to understand what plans are being preferred by the customers. Competitive analysis, customer’s financial profile, and market interest rate are some of the areas data analysts would put their minds on analyzing, and then devising a strategy and suitable plans for the market.

Startups

Innovation is on it’s all-time high right now. Subtract the Covid-19 era, we could see innovative solutions budding out from every direction. Most however were on the technology end, but industries like automobiles, construction, retail, manufacturing, and many others had their fair bit of share. Every time an entrepreneur comes up with an idea, it is critical to understand the market, what similar products are being served in the market, how the innovation is taken by potential customers, and if it is the right time to bring in the product. All these questions are answered by skill data analysts who gather relevant data and inform the business owners if the idea and timing are worth spending money and efforts on.

If you are thinking to step into the data analysis game, we recommend you enroll in our Data Science and Analytics Bootcamp offering all the in-demand skills including Transact-SQL, Microsoft Excel, Power BI, Data Visualization, and much more. Our bootcamps will also feature e-books, practice exams, certification training, live sessions with industry experts, and a 12-month job assistance post-graduation support policy.

Talk to our experts and discuss your future goals to get proper guidance on how you can master the art of data analysis and start your career as a skilled data analyst.