What Is The First Step As A Data Analyst?

Tools and skills for data analysis

Data analysis is important for businesses and organizations because it helps them make better decisions based on evidence and facts. Data analysts use various skills and tools to perform their tasks. Given below is the description of some of these tools and skills. 

Statistics: 

                Data analysts use different statistical techniques to summarize, describe, and infer patterns from data.

Coding languages: 

                 Data analysts use programming languages, like Python or R to manipulate, analyze, and visualize data.

SQL: 

         Data analysts use SQL (Structured Query Language) to query and manage data stored in relational databases.

Data visualization: 

                 Data analysts use data visualization tools, like charts, graphs, tables, or dashboards to present and communicate their findings to stakeholders or audiences.

Step #1. Define your objective

Defining your objective is the first step in any data analysis process. The good news is that objective defining can help you clarify what you want to achieve and how you will measure your success. For instance, if you are a data analyst for an online retailer, you may want to know how you can increase sales and customer satisfaction. 

In this case, you can offer free shipping and discounts to boost sales and customer satisfaction. For this hypothesis, you might measure the following metrics: sales revenue, number of orders, average order value, customer retention rate, and customer satisfaction score, to name a few.

Step #2. Collect data

Collecting data is the second step in the data analysis process. This process involves finding and acquiring the data to answer your question or test your hypothesis. For instance, if you are a data analyst for an online retailer, you might collect data from a website or app, Google Analytics, Shopify, Mailchimp, research reports, industry benchmarks, and competitor analysis, to name a few. 

The types and formats of data that you are working with depend on the sources and methods of data collection. Generally speaking, it’s possible to classify data into two types: structured data and unstructured data. 

Structured data has a predefined format and can be easily stored and processed in a database or spreadsheet, such as numerical data, categorical data, dates and times, and so on. 

Unstructured data has no predefined format and can be difficult to store and process in a database or spreadsheet, such as text data, images, videos, and audio files, to name a few.

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Step #3. Cleaning and preparing data 

This is the third step in the data analysis process. The purpose of this step is to make sure that the collected data is accurate, complete, consistent, and relevant for your objective. 

For instance, if you are going to analyze data of an online store, you can clean and prepare data by removing or imputing missing values, like empty cells, null, or N/A values. 

Similarly, you can transform and manipulate data, like converting data types, splitting or merging columns, aggregating or summarizing data, to name a few. 

The choice of techniques and tools to clean and prepare data depend on the types and formats of data that you are working with. Typically, you can use programming languages, like Python or R to perform data cleaning and preparation tasks on both structured and unstructured data. 

Apart from this, you can use software tools, like Excel, Power BI, and Tableau, to perform data cleaning and preparation tasks on structured data.

Step #4. Perform data analysis 

This step involves applying various methods and models to the data that you have cleaned and prepared to answer your question or test your hypothesis. For instance, if you are a data analyst for an online retailer, you might perform data analysis by exploring and visualizing data, like creating charts, graphs, tables, or dashboards to summarize and display data. 

In the same way, you can perform descriptive analysis, like calculating mean, median, mode, standard deviation, and frequency to describe the basic features of data. 

Besides, you can perform diagnostic analysis, like conducting hypothesis testing, ANOVA, and chi-square test to determine the causes or factors of data. Performing predictive analysis, such as using regression analysis, classification analysis, clustering analysis is a good idea to forecast or estimate future outcomes of data. 

Performing prescriptive analysis, such as using optimization techniques, simulation techniques, decision trees can recommend or suggest actions based on data. 

The choice of programming languages to perform data analysis depend on the types and formats of data that you are working with and the methods and models that you are applying. 

Typically, you can use programming languages, like Python or R to perform data analysis tasks on both structured and unstructured data. Apart from this, you can use software tools, like Excel, Power BI, and Tableau to perform data analysis tasks on structured data

Step #5. Interpreting and communicating results 

This step involves presenting and explaining your findings to stakeholders or audiences in a clear and concise way. For instance, if you are a data analyst for an online retailer, you might interpret and communicate results by summarizing and highlighting the key insights or takeaways from your data analysis. 

You can provide evidence or support for your insights or takeaways using data visualizations, statistics, or quotes. Apart from this, you can explain the implications or impacts of your insights or takeaways on the business or organization. 

And then you recommend or suggest for actions or improvements based on your insights or takeaways. The tools and techniques that you use to create data visualizations, reports, dashboards, or stories depend on the types and formats of data that you are working with and the preferences or expectations of your stakeholders or audiences. 

Normally, you can use Python or R to create data visualizations on both structured and unstructured data. Apart from this, you can use Excel, Power BI, or Tableau to create data visualizations, reports, dashboards, or stories on structured data.