Mastering the Technical Stacks: A Guide for Data & Analytics Professionals

Remaining on trend from last week's article, Navigating Different Roles in Data, Analytics & AI, let's look into the general skillset building blocks of three common roles:

1/ Data Analyst

2/ Data Scientist

3/ Data Engineer

Note to reader: this will not be covering the soft skillset or conceptual knowledge that is needed for each position.

So, here is you!

Bright-eyed and ready to learn the skills for enter the technology field. BUT, let's first understand what those technical skills are.

Data Analysts: The Insight Translators

Core Technical Skills:

• Programming Languages: most organisations use one of three programming languages for data analytics: Python, R or SAS (in order of popularity). Each has their own nuances and also libraries:Python Libraries: Pandas, NumPy, Seaborn, MatplotlibR Libraries: dplyr, ggplot2, tidyr, lubridate, plotly, tibble, readrSAS: not open-source so procedures are built-in
• SQL: for querying databases. Some statements include; SELECT, DISTINCT, WHERE, GROUP BY, ORDER BY, JOINS, common table expression (CTE) and more.
• Business Intelligence: Tools like Power BI and Tableau turn data into visuals that users can make sense of and then make business decisions.

Modern Tooling:

Data analysts may work in Azure Synapse, Alteryx or KNIME which are low-code solutions with drag & drop feature. These are very company dependent although Synapse is one of the most popular in Perth.

Data Engineers: The Architects of Data

Core Technical Skills:

• Programming Languages: For building and managing robust data pipelines, Python is going to be the most popular by far:Python Libraries: Pandas, SQLAlchemy, Requests, Apache Airflow, PySparkR Libraries: dplyr, DBI, tidyr, data.table, purrr, RPostgreSQL, RMySQL
• SQL: Both SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) form the backbone of data storage and retrieval.

Modern Tooling:

There is a huge range of tooling in the modern data engineering landscape that are all essential for scalable data storage and processing solutions (although not all needed at once!). They have numerous sub-services but if we categorise function we have the below popular tools:

• ETL Tools: Matillion, Fivetran, Airbyte, Talend, AWS Glue, Azure Data Factory
• Data Streaming: Kafka Confluent, AWS Kinesis, Azure Stream Analytics
• Data Orchestration: Airflow, Dagster, Prefect, Mage
• Data Warehousing: Snowflake, AWS Redshift, Azure, BigQuery, Databricks
• Data Transformation: dbt

Data Scientists: The Pioneers of Data-Driven Innovation

Core Technical Skills:

• Programming Languages: Python comes in at the most popular with Pandas, NumPy for data handling; Matplotlib, Seaborn for visualisation; Scikit-learn for machine learning, and TensorFlow, PyTorch for deep learning. The list goes on with new ones coming out everyday.Python Libraries: PyTorch, Scikit-learn, Keras, TensorFlow, pandas, NumPyR Libraries: caret, forecast, dplyr, ggplot2, tidyr
• SQL: used to retrieve and manipulate datasets in order to build models.

Modern Tooling:

• Common cloud services (AWS, GCP and Azure) offer Data Science solutions with high-code and low-code options e.g. Azure Machine Learning, AWS SageMaker, GCP Cloud Machine Learning.
• Modern Tools: KNIME, Databricks, Alteryx, QlikView, Palantir, RapidMiner
• This is not an exhaustive list as the choice of tools can vary widely across different organisations based on specific needs, existing infrastructure and budget.

Where next?

By no means a complete list, this is a snippet of the common tooling and technical skills that employers look for when hiring.

The question is...where do you learn these skills?

Check out this post for different avenues of learning in data & analytics

e.g. MOOC vs. Service Provider vs. Bootcamp vs. Degree

Good luck!

DR Analytics Recruitment

I'm Douglas - former data analyst and Founder of DR Analytics Recruitment. We grow people and businesses with an exclusive focus on the recruitment of data & analytics professionals. Companies use us because of our industry expertise, specialisation and technical testing.

Get in touch to learn more!

?? Email: [email protected]

?? Phone: +61 430 846 876

?? Website: https://www.analyticsrecruitment.com.au