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Data Analytics 101 : A Successful Kick Start

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Steps to Become a Great Data Analyst

A great data analyst requires skills and few steps that shall be followed. If you are pursuing a career as a data analyst, there are several steps that could be used as your guidance. First, earn a bachelor’s degree. The study field which emphasizes on statistical and analytical skills such as math or computer science would help you build the data analytics skill. Then, learn the important data analytics skills. As a data analyst, you will be faced with problems, bugs, and roadblocks, and therefore being able to solve your way out of them is a key skill that required.

In addition, you could also consider taking a certification. It will help you validate your skill and level of knowledge or proficiency among professionals. After that, get your first entry-level data analyst job. This job will help you understand how data analytics work in real-life. You could also implement your data analytics skills and knowledge if you are working as a data analyst job. Lastly, earn a master’s degree in data analytics because in order to be great at something, you have to keep learning. Earning a master’s degree in data analytics would enhance your ability and knowledge as well as opportunity to earn a bigger salary.

How Do You Start Data Analytics in Python?

There are several ways you can integrate python data analytics into your existing business intelligence and analytics tools. First, be familiar with python. Python programming language tends to be popular in the industry. It has complete packages that support the data analytics workflow. Then, learn pandas for analyzing, manipulating, and visualizing data. In order to using python, you should understand how to use pandas library which provides a high-performance data structure called DataFrame.

Furthermore, learn machine learning with scikit-learn. It is the most popular library for machine learning in python that could be used to build machine learning models to automatically predict the future or extract insights from data. Lastly, keep learning and practicing by finding your motivation that would encourage you to learn more.

Stages to Analyze Data with Python

Python is now becoming a popular multi-purpose language that has been widely used due to its flexibility as well as its extensive collection of libraries which are valuable for data analytics and complex calculations. To learn how to analyze data by using python, there are several stages that shall be learned.

First, learn how to import data sets. It lets you upload data from external sources and combine it with data which collected through analytics. You can use analytics to organize and analyze all data in ways that better reflect your business. After that, learn how to clean and prepare data for analysis. It is an important step prior to processing and often involves reformatting data, creating connections to data and combining data sets to enrich the data.

Moreover, you also need to learn how to summarize data. Summarizing the data is done to simplify the data and to identify what looks “normal” and what looks “odd”. Then, build a machine learning models using scikit-learn. The scikit-learn is also known as sklearn is a machine learning library used in python that provides many unsupervised and supervised learning algorithms. Lastly, build data pipelines architecture. It is a design and structure of code and system that copy, cleanse, transform, and route data source data to destination system such as data warehouse and data lakes.


Raw data provides no value. Otherwise, you do something to the data which would provide the value desired. Data analytics could include all steps that have been taken by enabling both human and machine to discover, interpret visualize, and tell stories of pattern in your data to drive business strategy and results.

Hornay, R., & Kidd, C. (n.d.). Data Analytics vs Data Analysis: What’s The Difference? BMC Blogs. Retrieved August 12, 2021, from https://www.bmc.com/blogs/data-analytics-vs-data-analysis/
How to Become a Data Analyst in 2021. (2021, March 17). Master's in Data Science. https://www.mastersindatascience.org/careers/data-analyst/
Williams, D. (2021, March 15). 5 Proven Techniques to Improve Service Desk Optimisation. Servicely.Ai. https://www.servicely.ai/post/5-proven-techniques-to-improve-service-desk-optimisation
What is Data Preparation? (+ How to Make It Easier) - Talend. (2021, January 5). Talend Real-Time Open Source Data Integration Software. https://www.talend.com/resources/what-is-data-preparation/#:%7E:text=Data%20preparation%20is%20the%20process,data%20sets%20to%20enrich%20data.

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