At its core, Data Analytics is simple: Make sense of data and turn it into useful insights.
But getting this far needs many steps and each step has different tools. So where does one even start ?
Let’s look at some of the steps and some of the tools they use
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Data Collection
- Collecting relevant data from various sources.
- Tools: SQL, Python (requests, web scraping with BeautifulSoup, Selenium), R (rvest)
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Data Cleaning/Preparation
- Preparing and structuring data for analysis by handling missing values, correcting errors, and transforming formats.
- Tools: Python (Pandas, NumPy), R (dplyr, tidyr), Excel, Alteryx, Apache Spark, SQL, Trifacta
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Data Analysis
- Applying statistical and computational methods to interpret data and extract insights.
- Tools: Python (Pandas, Scikit-Learn, SciPy), R (caret, stats), Excel, SPSS, SAS, Apache Spark
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Data Visualization
- Presenting data in visual formats to enhance understanding and communicate findings.
- Tools: Tableau, Power BI, Python (Matplotlib, Seaborn, Plotly), R (ggplot2), Excel
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Machine Learning/Modeling
- Using algorithms to create predictive or prescriptive models based on data.
- Tools: Python (Scikit-Learn, TensorFlow, Keras), R (caret), Spark MLlib, Databricks, Google Colab
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Data Storage/Warehousing
- Storing large volumes of data for quick access and analysis.
- Tools: Amazon Redshift, Google BigQuery, Snowflake, SQL databases (PostgreSQL, MySQL), Microsoft Azure
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Business Intelligence (BI)
- Generating actionable insights and analytics for business decision-making.
- Tools: Tableau, Power BI, Looker, Qlik, Qlik Sense, Microsoft Excel
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Reporting and Dashboards
- Creating automated reports and dashboards to monitor data and key metrics.
- Tools: Tableau, Power BI, Looker, QlikView, Google Data Studio, Excel
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ETL (Extract, Transform, Load)
- Extracting data from sources, transforming it to fit the analysis, and loading it into storage or visualization tools.
- Tools: Apache NiFi, Talend, Alteryx, Informatica, Airflow, SQL
If you have a specific, well defined requirement in a given environment then you need to pick a specific tool.
But if you are looking to enter the area or improve your skillset, note that a few tools appear in many steps:
- Python: Collection, Cleaning, Prep, Analysis, Visualization, Machine learning
- SQL: Collection, Cleaning, Prep
- Excel: Cleaning, Prep, Analysis, Visualization, BI, Dashboards
- Tableau or Power BI: Analysis, Visualization, BI, Dashboard
These 4 tools are a great place to start if you’re trying to expand your skillset.
Here’s some more tips on where to start:
- If you are a complete newbie, start with Excel
- If you are already familiar with Excel, Tableau would be a good place to start
- If you are familiar with programming ( in any language), Data Analysis with Python would be a good place to start
- If you are mostly interested in Dashboards and Presentation, Tableau or PowerBI would be the right starting point
- If you are looking for a flexible tool that can help in almost all the steps then Python is good. But remember that Python has a learning curve and you have to master several libraries for each step
- If you are mostly interested in databases and the technical aspects of data, SQL is a good starting point
All tools overlap and all have unique aspects. But you cannot go wrong with these four.