Learning Data Analytics without a coding background
This guide is specifically written to help take the proper steps toward a career in data analytics even without a coding background.
We live in the age of data, and data analytics professionals are currently high in demand. The field has usually been dominated by individuals with strong programming backgrounds. However, the scenario is changing now. With user-friendly data tools and platforms, everyone can become a data analyst, even without a coding background.
We shall look into the different roles associated with data analytics and specify the principal competencies for each of those roles. Following this, we will give a staged roadmap for those who intend to become data analysts by constructing a robust business analytics foundation. By the end of this article, you will have a very clear sense of how to work to your strengths in excelling with regard to data analytics.
Skills and roles in the Data Science world
While coding is often associated with data analytics, it’s essential to understand that different roles demand varying skill sets. Let’s break down the key competencies for three primary roles:
1. Data Analyst
A good data analyst needs a varied skill set, marrying technical expertise with strong analytic abilities. Here is a closer breakdown of what makes the right kind of skills in this regard:
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Knowledge of Statistics
For correct data interpretation, an adequate understanding of statistics is important. The concepts of statistics include measures of central tendency, variability measures, probability distributions, hypothesis testing, and regression analysis. These techniques of statistics are helpful in understanding different patterns of data and support a relevant set of inferences.
MS Excel
Excel is the basic, and the most important tool for all data analysts. Mastering Excel means the ability to manipulate and visualize data with the help of formulas, pivot tables, and charts. Advanced functions such as XLOOKUP, INDEX-MATCH, and data validation are needed in structuring and analysing large data. Skilful Excel use allows doing all the manipulations over data and preliminary analytics without turning to more difficult tools. Within Excel, Power Query is an extremely useful data transformation tool to easily perform some complex transformations on data.
Knowledge of SQL
SQL is important in querying and managing databases. A data analyst has to have the ability to write SQL queries in extracting, filtering, and aggregating data from relational databases. One should be able to join tables, use subqueries, or create views to deal with large amounts of data and gain the prowess to undertake in-depth analysis on it.
Data Pre-processing
Most of the time, data needs to be cleaned and pre-processed before any analysis. Treatment of missing values, duplicate value removal, inconsistency resolution, and format conversions are activities that could be undertaken in this regard. A great deal of care and attention has to be devoted to the processing of data with an eye on quality and accuracy.
Data Visualisation Tools
Business Intelligence (BI) tools are important for data analysts to create interactive, user-friendly dashboards and reports with simple drag-and-drop functionality. Data visualization is an important part of any data analyst’s daily job. Tools like Tableau and PowerBI help in the clear and effective communication of insights to stakeholders by providing them with an understanding of complex data.
2. Data Engineer
A data engineer in a data ecosystem is important in building and maintaining the infrastructure that empowers data generation and analysis. The following are the essential skills for a data engineer:
Database Management
Data engineer needs to have a good grasp of relational and non-relational database management concepts. It means knowledge about database design, indexing, normalization, and optimization techniques. He would be required to know about SQL databases like MySQL or PostgreSQL, or a NoSQL database like MongoDB or Cassandra for quite effective and efficient storing and retrieval of data.
ETL (Extract, Transform and Load) Process
Since data integration is intrinsic to ETL processes, a data engineer should be able to design and develop robust ETL pipelines for the extraction of data from multiple sources, transformation into a format that is usable, and finally, loading into data warehouses or databases. Some of the tools used in doing these tasks are Apache Nifi, Talend, and AWS Glue.
Big Data Technology
Over the last decade, big data technologies have gained importance as data is growing in an exponential manner. Data engineers need to have prior knowledge of frameworks like Hadoop and Apache Spark, designed to process large datasets. Distributed computing concepts, along with hands-on experience with Hive, Pig, and Kafka, should be an integral part of his/her repertoire.
Knowledge of Python
A data engineer is incomplete without programming skills in languages like Python and Scala. Python in particular is essential for data processing tasks as it is widely used due to the availability of extensive libraries like Pandas, NumPy, and PySpark.
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Cloud Computing
Cloud platforms provide flexible and scalable solutions for data storage and processing. Every data engineer must be exposed to cloud services like S3, EC2, Lambda, BigQuery, etc. provided by platforms like AWS, Google and Azure, and know how to utilize them when constructing and managing data infrastructure.
3. Data Scientist
A Data Scientist brings together experience in statistics, programming, and domain knowledge to derive insights from data. Listed here are the key competencies needed for this job profile:
Statistical and Mathematical Knowledge
Data scientists need to know about statistics and mathematics in depth. This includes theory of probability, tests in statistics, regression, and algorithms of machine learning. All of these form the foundation for the development of models and the interpretation of data with accuracy.
Programming Skills
This includes proficiency in programming languages such as Python and R, which have extensive libraries in those languages, particularly in NumPy, pandas, sci-kit-learn, and TensorFlow when doing data manipulation, statistical analysis, and machine learning. Writing clean and efficient code will pay off in the implementation and testing of models. However, there are no-code tools like KNIME and Rapidminer, available with a GUI-based interface for data science tasks. They allow users to create workflows with drag and drop functionality to perform data mining and create machine learning models.
Data Wrangling
Data scientists must be proficient in cleaning and preprocessing data. It involves handling missing values, outliers, and converting the data into a format suitable for analysis. All these are accomplished by tools like Pandas in Python.
Machine Learning
Data scientists need to know machine learning algorithms and techniques, including supervised learning, for instance, regression and classification, unsupervised learning in clustering and dimensionality reduction, deep learning, and deep learning frameworks such as TensorFlow, Keras, and PyTorch.
Data Visualisation
Communication of insights done well is going to require skill with data visualization tools. This will mean using libraries in Python like Matplotlib, Seaborn, and Plotly, or specialist tools like Tableau to build a rich visualization from data.
Roadmap for Learning Data Analytics
The journey to becoming a data analyst, with a specific view toward business analytics, is an exciting and daunting adventure. The roadmap below details how to set up the building process and further develop into a proficient data analyst, even from scratch.
Build Basic Foundation
Understand clearly what data analytics is and why it is essential. Analytics types have been classified into four classes: Descriptive, Diagnostic, Predictive, and Prescriptive.
A firm composite of basic statistics is required for data analysis. Understanding the following key concepts in detail: mean, median, mode, variance, standard deviation, probability, and distributions. Several of the basic mathematical concepts including elementary algebra, calculus, and linear algebra, many of the analytical methods build on these concepts.
Become Proficient in Excel
Excel is an analyst’s basic tool. It’s simple enough, though loaded with many features, so you can learn from the easiest functions to the most complicated all at your own pace. This entails the fundamental navigation of Excel, creating a spreadsheet, and the use of basic formulas. From here, master the application of Pivot Tables, VLOOKUPs, INDEX-MATCH, data validation, and advanced charting.
Learn Data Visualisation
Data visualization is brought in to properly convey insights. Learn the principles of good visualization and why you should tell stories with your data. Get familiar with tools like Tableau, Power BI, and Google Data Studio.
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Learn SQL
Learn SQL; it forms the basic foundation for querying and extraction of data in databases: SELECT, FROM, WHERE, and JOIN are some of the most basic SQL commands. Get to learn more advanced queries, which include subqueries, window functions, and optimizing queries.
Become good at Data Pre-processing
Data Cleaning and Data Preprocessing: Handling the missing values, removing duplicates, correcting inconsistencies, etc.
Develop Domain Knowledge
A data analyst should understand the business context. Learn about an industry of your choice: whether it be finance, marketing, healthcare, etc., and learn the KPIs important to your domain and how to measure their performance.
Gain Practical Experience
Apply skills with real-world projects and practice datasets. Engage in projects or internships to get hands-on experience, participate in Kaggle competitions, and share your projects on GitHub. Regularly practice by working on new datasets and improving your analysis techniques.
Conclusion
This article was intended to provide you with a roadmap to learn and get into the Data Analytics field. However, the first step is to choose the role such as Data Analyst, Data Engineer, or Data Scientist that suits you. This will help you tailor a learning path towards any of these career aspirations.
Techcanvass can help you in choosing the right career with guidance from an expert. You can also join one of the courses, once you are clear with your career path:
Data Analytics Certification Training: For comprehensive training and learning experience from scratch.
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Power BI Training: If you are already into Data Science or Data analysis career, you can upskill with Power BI, one of the top Data visualization tools.
