How do I become a data scientist without programming knowledge

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The primary goal of the data science field is to extract insights, from data. Data scientists employ techniques, from computer science, machine learning and statistics to convert data into knowledge.

Data scientists have day to day responsibilities, including:

  • Gathering, sorting and organizing data from sources.
  • Conducting analysis on the data to uncover patterns, trends and relationships.
  • Creating models and implementing machine learning algorithms.
  • Designing dashboards and visualizations to effectively communicate insights.
  • Providing data driven recommendations, to stakeholders.

Is Programming Required to Become a Data Scientist?

While many associate data science with computer programming, strictly speaking, writing code is not always necessary. The data science landscape has expanded with various no-code and low-code tools that allow those without a formal programming background to accomplish various tasks.

That said, having some basic scripting or programming knowledge can be useful for handling data processing, analysis, and modeling. Most data scientists have some competency in languages like SQL, Python, and R. But it is possible to get started without writing much custom code.

The key is leveraging various user-friendly analytics, business intelligence, and data science platforms that don’t require coding. We’ll explore some of these tools further in the next sections.

Learn Spreadsheets In Depth

Spreadsheets can be a useful starting place for people who are new to data science and analytics before they learn more complex platforms. Common options include programmes like Apache OpenOffice Calc, Google Sheets, and Microsoft Excel.

Data Scientist

Spend time becoming an expert in using spreadsheet software:

  • Learn all aspects of data manipulation from sorting, filtering, formatting and using formulas for calculations.
  • Understand how to create different charts and visualizations to uncover insights.
  • Use built-in capabilities for forecasting trends, assessing correlations, and performing regression analysis.
  • Learn how to record macros to automate repetitive tasks.

Building spreadsheet skills establishes fundamentals around working with data programmatically while avoiding the need to code. This experience can then help transition to other platforms.

Choose No-Code/Low-Code Analytics & Data Science Platforms

There are now various software options that enable building analytical models and machine learning models using intuitive visual interfaces instead of programming languages. These platforms help democratize data science by making it more accessible.

Some top no-code and low-code analytics and data science platforms to consider learning include:

Tableau

  • Drag-and-drop interface to easily visualize, analyze and build interactive data dashboards. Can also leverage AI capabilities through integrations.

Knime

  • Open-source platform for creating data pipelines and models through a visual workflow builder instead of coding. Interface is module-based allowing limitless customization.

RapidMiner

  • Leading unified platform for data preparation, visualization, modeling, and deployment. Graphical “process” design avoids need for programming expertise.

Azure Machine Learning

  • Tool for building, training, deploying and managing machine learning models using intuitive interface backed by Microsoft Azure. Integrates seamlessly with other Azure data and analytics services.

SPSS Modeler

  • Predictive analytics platform focused on both classification and regression modeling featuring multiple algorithms and modeling methods. Visual interface streamlines model creation workflows.

Ludwig

  • Open source machine learning toolbox built on top of TensorFlow that allows training models without needing to code through its command line tool and Python API.

As can be seen there is an abundance of options. Evaluate various platforms to choose one or two that best meet your analytical objectives. Using these will enable developing data science skills without needing to become an expert coder.

Learn Structured Query Language (SQL)

While the previous platforms help circumvent extensive programming, taking the time to learn SQL can still be extremely valuable for those pursuing careers in data.

SQL is the universal language used for managing, manipulating, and analyzing data stored in relational databases. Learning SQL gives a foundational data skillset for:

  • Extracting datasets for analysis using SELECT queries
  • Filtering and ordering results using WHERE and ORDER BY clauses
  • Performing aggregation functions such as counts, sums etc. using GROUP BY
  • Utilizing JOINS to connect data from multiple tables
  • Building database objects like views and indexes to optimize data operations

Strong SQL skills empower accessing and preparing high quality datasets for use in analytics and modeling workflows. The query language maps nicely to logical business questions making it broadly useful.

There are many online courses to choose from to develop core competency. Additionally, hands-on practice using SQL against actual datasets will accelerate learning.

Learn Data Visualization Best Practices

Effectively communicating data insights and findings to others is a key expectation of data scientists. Strong data visualization and storytelling skills are essential even for those who don’t code.

Using the analytics/BI tools mentioned previously, spend time creating compelling visualizations that intuitively showcase important trends and relationships in data:

  • Master how to visualize comparison, ranking, correlation, distribution, composition, geospatial, and temporal data
  • Learn techniques for visualizing machine learning model performance
  • Become skilled in dashboard design principles and information layout best practices
  • Understand how to make interactive visuals tied to user inputs

Additionally, learn how to contextualize and present data visualizations so they clearly support conclusions and aid business decision making.

Develop Statistical, Analytical, and Critical Thinking Abilities

While software, tools and coding aid the mechanics of analyzing data, sound logical reasoning skills make the difference in properly interpreting what data means.

Expertise applying various statistical and analytical techniques such as hypothesis testing, regression analysis, experimental design, and multivariate testing separates quality data scientists.

Data Scientist

Make developing these core skills a priority:

  • Take online courses in probability, statistics, quantitative reasoning
  • Learn how to use analysis techniques beyond just descriptive statistics
  • Understand how to correctly determine causation instead of just correlation
  • Recognize how to avoid biases, sampling errors, overgeneralization, and anecdotal reasoning

Strengthening analytical thinking and statistical acumen provides a lens to discern meaningful and significant patterns from inconsequential noise.

Become an Expert in Excel Power Tools

We’ve already covered the importance of spreadsheet software. To take Excel skills to an advanced level, become proficient utilizing its power tools add-ins:

Power Query – Data preparation tool for reshaping, cleansing, and enriching source data prior to import into data models.

Power Pivot – Build complex data models by importing and relating datasets to enable powerful data analysis using pivot tables, charts, filters etc.

Power View – Interactive data visualization tool for analyzing Excel data models using compelling charts, maps, pivot tables and other visuals.

Power BI – Publish Excel data models to the Power BI cloud service to build feature-rich interactive reports and dashboards which can be shared with others.

Learning this toolset helps construct full-fledged analytics solutions leveraging Excel’s powerful in-memory database engine without leaving the comforts of the familiar spreadsheet environment or needing to code.

Pursue Data Science certifications

Formal training and certifications can provide structured depth and breadth developing data science skills, making up for professional work experience.

Reputable certificates to consider pursuing include:

IBM Data Science Professional Certificate – Covers data science methodology including tools, data analysis, data visualization, machine learning, Python libraries, SQL, NoSQL databases, and Apache Spark. Available through Coursera.

Microsoft Certified: Data Analyst Associate – Develops SQL, Power BI, Excel analytical skills. Topics span data visualization, modeling, analytics, and machine learning fundamentals.

Google Data Analytics Certificate – Created by Google, coursework focuses on data preparation, analysis, visualization, storytelling. Taught using real-world examples and case studies. Offered on Coursera.

SAS Certified Data Scientist – Validates proficiency using SAS to manage, analyze, visualize data, and build/deploy models. Covers machine learning techniques.

These credentials help validate core data science abilities without needing programming expertise.

By supplementing certificates with hands-on self-study developing projects for a portfolio, professional opportunities can be obtained.

Data Scientist

Next Steps In Your Data Science Journey

Gaining proficiency across the various aforementioned areas sets the stage for a promising data science career even without prior programming experience.

From here continue expanding knowledge by:

  • Learning dashboards development best practices
  • Keeping up-to-date on latest data science and analytics trends
  • Understanding tools like Hadoop, Spark, cloud data platforms among others
  • Learning basics around big data architectures, database systems, distributed computing
  • Enhancing data storytelling and executive presentation abilities
  • Building leadership, collaboration and stakeholder engagement skills

While mastering programming may still help progress further, possessing broad data skills across multiple areas gives flexibility to grow professionally.

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