What is Data Analysis? My intake from Google Data analysis course

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I got the opportunity to take the new google data analytics course. This is the gist of my understanding of the data analytics course by Google.

Data analysis is all about dive deep into the ocean of data and finds out interesting facts which could drive any kind of decision-making.

in theory, it is the collection, transformation, and organization of data in order to draw conclusions, make predictions and drive informed decision making.

Here the business knowledge is one of the essential things for the Data Ecosystem. you may have found the patterns or connected the dots in the data but if you don’t know the underlying business logic behind the data, there is no sense of the pattern. The best formula for any data analyst would be

Data + business knowledge = mystery solved

So you need data knowledge and you should gain as much as business knowledge possible to make sense out of data.

The typical data-driven business decision-making process involves the following steps-

  1. Ask questions and define the problem.
  2. Prepare data by collecting and storing the information.
  3. Process data by cleaning and checking the information.
  4. Analyze data to find patterns, relationships, and trends.
  5. Share data with your audience.
  6. Act on the data and use the analysis results. [most important part]

The data analyst always needs to understand more about the situation based on facts from the data. They definitely have some gut feeling like others have, but they try to evaluate the feelings using a fact-based approach.

What are the skills the data analyst must have?:

  1. Curiosity
  2. Having technical mindset
  3. Data design
  4. Data strategy

Let’s deep dive into each of these categories. Curiosity means you have keen to understand the problem, you ask questions and try to answer using facts.

Technical mindset means, you chunk down the problems into smaller pieces and later connecting them together to solve the business problem.

Data design means how do you arrange the data to present it. Once you have arranged the data it becomes very easy to solve the problem using data.

Data strategy: it is all about the data life cycle from initiation of a project to the end of the project. It consists of things like which data do you need, what are the data sources, what technical skills and peoples do you need to accomplish the project etc. etc.

Now come to the part of analytical thinking. It is made out of five pillars- Visualization, Strategy, Problem statement, Correlation, Big picture and detailed oriented thinking.

Once you understand the business problem and gathered all the data, you visualize the data using any tools (like Excel, Tableau, Looker, etc. ). Using visualization, it is becoming very easy to understand the underlying logic behind the data. You will find relationships between the problem statement and different data plots. By the way, you could use SQL for the data arrangement if the database becomes very high.

You often find that there are correlations between the data. But remember, correlations don’t mean causation. for example if a company has two products and they observe that for the last one year, both of their product sales have increased dramatically. Both of their product sales growth is correlated to each other but the products are totally different. One is a live streaming subscription service and the other one is their retail store services. When you deep dive into the problem, you will find that their underlying cause of growth is totally different from each other. You should always look for these signs during your analysis.

Once you have done, problem statement analysis through visualization, then comes to the part of the context behind your analysis. You are trying to find the bigger picture behind all your analysis. Once you have done that, you just convey your findings to the respective audiences. Trust me this is one of the best parts of any kind of analysis. You find something totally new which the other stakeholders haven’t thought out before and you direct them through your analysis. That is the success of data analysts like you.

What tools the data analysts are usually use? There are lot of options are out there but popular ones are Microsoft Excel, Google Spreadsheet, SQL for data management, manipulation, and Tableau, Looker, Power BI for data visualization. Although both excel and SQL could work in the same way, excel is best suited for small-scale data analysis and SQL is for large-scale datasets.

Here you could find the best online resources out there for the above tools learning:

Spreadsheet learning links:

  1. https://support.google.com/a/users/answer/9282959?visit_id=637361702049227170-1815413770&rd=1
  2. https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb?ui=en-US&rs=en-US&ad=US
  3. https://support.google.com/a/users/answer/9300022

SQL learning links:

  1. https://www.w3schools.com/sql/default.asp
  2. https://towardsdatascience.com/sql-cheat-sheet-776f8e3189fa

I hope you find this article informative. Please let me know your thoughts down in the comments below. You could also share your intakes, thoughts about the data analyst profile and its future prospects.

Here is the Google data analyst course link: https://coursera.org/share/6422d453e6f2ea812745d6496f46dc32

I will see you soon with another informative article.

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Originally Published on LinkedIn on 27th March 2021: https://www.linkedin.com/post/edit/6780346699379433472/

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Business Analyst/ Data Scientist at Genpact https://www.linkedin.com/in/soumyabratar/