Data Analyst Toolkit

As a data analyst there is a wild range of tools you need for exploration, analysis, visualization and prediction. I thought this was good opportunity to explore the most popular and go to tools for data analyst.

Stage 0: Identify Business Questions.

These tools are critical for collection thoughts and ideas from stakeholders. This is the most important step in data analysis life cycle. Also the majority of your key performance indicators will be identify at this stage. If the analyst doesn't take this stage serious, it will extend the life of the data analysis project. After this meeting, you should have agreement on the direction of the analysis project. 

  • Note Pad 
  • White board 
  •  An Open Mind and Ears

After this stage send notes and summary of objectives and deliverables.

Stage 1: Data Collection 

  • Excel/Google Sheets-Most of data really still comes from spreadsheets, get used to this.
  • Database-second most popular tool for storing data. Get familiar with SQL and SQL Server Analysis Services(SSAS)
  • API-This can be heaven or hell depending on the out of the data.

Stage 2: Data Cleansing and Modeling

This is the most time consuming part of the data analysis project life cycle. We have Pandas and Power BI tutorials

  • Excel Power Pivot- you can bodel data by merge and appending tables in an excel workbook from multiple sources
  • Python  Pandas- the data analysis library in Python that allows for manipulation and statistical analysis. 
  • SQL-joins, merges,filters all can be done in SQL prior to export.
  • Alteryx- growing ETL tool that is a full service data cleansing powerhouse. 
  • Power BI-Essentially, this is Power Pivot on steroids with a visualization component.

Check out Alteryx for Beginners 

Stage 3: Data Analysis

  • Excel-the data analysis toolkit, pivot tables and summary statistics 
  • Pandas-the best library for analysis of tabular data.
  • Matplotlib/Seaborn- these are great tools to visualize your data. It's not suitable for dashboards but great for exploratory data analysis. 
  • SQL-there are tons of function for analysis and summarizations.
  • Power BI-personally my go to tool for analysis. New features being created to improve AI and BI functionality.

Stage 4: Data Visualization:

  • Tableau- this is the most senior and most powerful data visualization tool on the market. You can create stunning dashboards, data stories and animations.
  • Power BI- has a very strong set visuals and custom visuals that rival Tableau. It just shy of taking the throne. 
  • Google Data Studio-Google data visualization tool is new but just as comparable to the other big 2. 

Stage 3: Present Findings 

  1. PowerPoint
  2. Power BI Report
  3. PDF report

Gaelim Holland

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