• Data Analyst
  • The goal of any data analysis project is to provide information that helps you make informed business decisions. There are typically five loops in the data analyst job description process.The role of data analyst is to identify the data to be analyzed ,get the information to be analyzed ,clean the data and prepare it for analysis, analyze data, deriving meaning from analysis

    Various skills required for becoming a succesful Data Analyst are as follows:

    1. Data Visualization

    Data visualization is a person’s ability to present data findings via graphics or other illustrations. The purpose of this is simple:It facilitates a better understanding of data-driven insights, even for those who aren’t trained in data analysis.With data visualization, data analysts can help a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance.

    This capability empowers you the data analyst to gain a better understanding of a company’s situation, convey useful insights to team leaders, and even shape company decision-making for the better.Data visualization may even allow you to accomplish more than data analysts traditionally have.


  • Coursera- https://www.coursera.org/courses?query=data%20visualization
  • Udemy - https://www.udemy.com/topic/data-visualization/
  • Harvard- https://pll.harvard.edu/course/data-science-visualization.(FREE)
  • 2. Data Cleaning

    Cleaning is an invaluable part of achieving success and data cleaning is no different.It’s one of the most critical steps in assembling a functional machine learning model and often comprises a significant chunk of any data analyst’s day.With a properly cleaned dataset, even simple algorithms can generate remarkable insights.uncleaned data can produce misleading patterns and lead a business towards mistaken conclusions.Data analyst qualifications require proper data cleaning skills and there are no two ways around that.


  • Class central- https://www.classcentral.com/course/getdata-1714 (free)
  • Coursera - https://www.coursera.org/learn/data-cleaning(paid)
  • Udemy - https://www.udemy.com/topic/data-cleaning/ (paid)
  • 3. MATLAB

    MATLAB is a programming language and multi-paradigm numerical computing environment that supports algorithm implementation, matrix manipulations, and data plotting, among other functions. Businesses interested in big data have begun turning to MATLAB because it allows analysts to drastically cut down on the time they usually spend pre-processing data and facilitates quick data cleaning, organization, and visualization. Most notably, MATLAB can execute any machine learning models built in its environment across multiple platforms.


  • Great learning - https://www.mygreatlearning.com/academy/learn-for-free/courses/matlab
  • Matlab academy - https://matlabacademy.mathworks.com/
  • 4. R

    R is one of the most pervasive and well-used languages in data analytics. One poll conducted by the Institute of Electrical and Electronics Engineers’s (IEEE) professional journal, Spectrum, found that R ranked fifth in a list of the top ten programming languages used in 2019.R’s syntax and structure were created to support analytical work; it encompasses several built-in, easy-to-use data organization commands by default. The programming language also appeals to businesses because it can handle complex or large quantities of data.


  • Data camp - https://www.datacamp.com/courses/free-introduction-to-r(paid)
  • Great learning - https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-r (paid)
  • 5. Python

    This high-level, general purpose programming language landed the number one spot in IEEE’s Spectrum 2019 survey, and for a good reason — it offers a remarkable number of specialized libraries, many of which pertain specifically to artificial intelligence (AI).Python is a skill , data analysts need to keep current in an increasingly AI-concerned professional landscape. Those interested in furthering their familiarity of Python should also look into its ancillary programs such as Pandas (an open-source data analysis tool that works in symbiosis with Python’s programming language) or NumPy, a package which assists Python users with scientific computing tasks.


  • Coursera
  • freecodecamp
  • Codecademy
  • 6. SQL and NoSQL

    there are several database languages that you will need to be familiar with Structured Query Language, better known by its acronym, SQL.SQL persists as the standard means for querying and handling data in relational databases.


  • Coursera :Beginner : https://www.coursera.org/learn/introduction-to-nosql-databases , Advanced : https://www.coursera.org/specializations/nosql-big-data-and-spark-foundations
  • Great learning - https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-r (paid)
  • 7.Machine Learning

    Statista research indicates that artificial intelligence and predictive analytics comprise significant areas of investment right now. While not all analysts will find themselves working on machine learning projects, having a general understanding of related tools and concepts may give you an edge over competitors during your job search.


  • Google (FREE) : https://developers.google.com/machine-learning/crash-course
  • Top Universities: https://www.freecodecamp.org/news/best-machine-learning-courses/
  • 8.Microsoft Excel

    Stressing the importance of Microsoft Excel skills almost seems laughable when one considers the significantly more advanced technology data analysts have at their disposal.Excel is well-used among businesses.


  • Internshala : https://trainings.internshala.com/excel-course/?utm_source=Google-Search&utm_campaign=CT-Search-Exact-Excel-
  • 9.Key Soft Skills Data Analysts Need
    Critical Thinking

    It’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights and information from the information you have at hand.


    Being a good data analyst effectively means becoming “bilingual.”You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports rather than confuses — business-centered decision-makers