To be a Data Analyst, companies expect the below categories of skills. From each category, you can master one to two skills.
Programming Skills: Learn Python or R Programming
Database Skills: Learn MySQL, SQL Server, PL/SQL, Snowflake, Excel
Visualization Skills: Learn Power BI, Tableau, Excel or Looker
Soft Skills: Analytical Thinking, Problem-Solving & Business Acumen
Statistical Skills: Basic Statistical Knowledge or Advanced Statistical Knowledge
From the above five categories, you can master at least one skill.
- Person A plans to learn skill combinations like Python, MySQL, Power BI, Analytical Thinking and Basic Statistics.
- Person B plans to Learn skill combinations like Python, MySQL, Tableau, Problem-Solving, Basic Statistics and Advanced Statistics.
Companies are expecting a minimum of one skillset from each category. So, if you plan to get into Data Analytics, try to learn these skill combinations.
What is the purpose of different tools in Data Analyst Job Profile?
A Data Analyst’s crucial skills will be Analytical Thinking, Data Visualization Skills & Database skills. As a day-to-day lifecycle, we try to develop various Analytical Dashboards using Power BI/Tableau/Looker. To establish those visualizations, we collect data from multiple data sources. To handle the data sources, knowledge of SQL is essential.
Once you collect data from the database into Data visualization tools, you must have the analytical thinking ability to present the right metrics/KPIs to the client/stakeholder.
Why do I need to Learn Programming Skills?
Let me explain; Programming helps professionals to perform more customized tasks which you cannot function in Tableau/Power BI/SQL.
It can be any customized task like Customized Data Cleaning, customized data understanding, customized data visualization, etc.
So, learning programming skills will be essential and crucial for being a Data Analyst.
When it comes to statistics, Statistics is the rule book which helps us do our analysis correctly. It helps us to gauge our investigation concerning statistics. Statistics explain us to understand the ambiguity of data and allow us to handle it better. So we can precisely communicate the results to the clients or stakeholders.
If anyone is planning to be Data Analyst, please start with SQL, Python & Power BI, Which will be helpful for your career transition.
What to Learn in Python for Data Analysis?
To be a hero in Python? Learn below important topics in Python for Data Analysis.
- Learn Python Data Types like Integer, Float, String, Complex Numbers
- Learn Python Arithmetic Operators
- Learn Python Data Structures like List, Tuple, Set & Dict
- Learn Python String Manipulations
- Learn Python Regular Expressions & Exception Handling
- Learn Python Control Flow & User-Defined Functions
- Learn Python Lambda Functions
- Learn Python Libraries for Data Analysis like Pandas, Numpy, Scipy, Matplotlib & Seaborn.
What to Learn in SQL for Data Analysis?
To be a hero in SQL? Learn below important topics in SQL for Data Analysis.
- Learn SQL Data Types & DDL Commands
- Learn SQL Syntax related to DML
- Learn Retrieving Data with SELECT
- Learn Data Filtering, Sorting & Limiting Syntax
- Learn SQL Joins
- Learn Data Manipulation using SQL
- Learn SQL Functions for Data Analysis
- Learn SQL Stored Procedures.
What to learn in Power BI for Data Analysis?
To be a hero in Power BI, Learn below important topics in Power BI for Data Analysis
- Learn Power BI Different Data Visualization Charts
- Learn Power BI Data Cleaning with Power Query
- Learn Power BI Integration with Python
- Learn Power BI Data Modelling
- Learn Power BI Filters
- Learn Power BI DAX Calculations like YoY, AVERAGE, SUM, MIN, SUMX, CALCULATE, etc.
- Learn Power BI Advanced Visualizations
- Learn Power BI Server
- Learn Power BI Dashboard Creation
- Learn Power BI Storytelling
After completing all the above syllabi, work on real-time projects, place them in your resume, and start with your roller coaster ride.
I hope you like the article and that It’s informative. Contact me on my Instagram, LinkedIn, or YouTube for any assistance.
You can join even my Full Stack Data Analytics Program with Weekday Live Classes for 3 Months + 3-6Months Internship, CLICK HERE
What is Data Analytics Course For?
Data Analytics Course enables people to perform Descriptive analytics using tools like Python, Power BI, Tableau, MySQL, Statistics & Excel. Why use Descriptive Analytics? To provide monthly reports/weekly/yearly reports with the stakeholders/managers by using tools like Power BI & MySQL. So that they can make data-driven decisions with the help of KPI Dashboards developed by Data Analysts.
Data Analytics Course Syllabus:
To be a Job-Ready Data Analyst, Learn this Data Analytics Course Syllabus. First, starting with Python, Power BI, MySQL, Statistics, Excel, Analytical Thinking & Problem Solving is essential. Focus on Practical knowledge and develop confidence in handling the projects.
Is Data Analytics tough to study?
You learn Topics like Python, MySQL, Power BI, Tableau, and Excel are very easy to understand. You can learn them quickly with more assignments and projects. But Analytical Thinking, Problem-Solving, and Approach explanation are Hard. So Allocate your time accordingly. With consistency and grit, you can achieve your transition 100%
Who is eligible for the Data Analytics Course:
Anyone with Any Degree/Any Engineering can take up a Data Analytics Career. Even non-programmers can take up Data Analytics Career but need to give extra time to learn to program.
- If you feel dealing with data interests you, joining Data Analytics would be the best option.
- If you feel data science is complicated and want to start your career in the Data field, Start with Data Analytics.
Does Data Analytics require coding?
Yes, demonstrating moderate knowledge of programming tools will also help you in interviews and job profiles. For example, some companies are hiring Data Analysts without coding knowledge. But having coding knowledge of tools like Python or R Programming is an advantage in interviews.
Is Data Analytics Good Career:
The US Bureau of Labor Statistics estimates the growth of Data Analytics jobs by 23% from 2021 to 2031, growing at a very great speed when compared with other industries
Can a fresher become a data analyst?
The answer is a big “YES.” You can become Data Analyst as Fresher, but you must focus on Real-Time Projects in your Resume. How to get real-time projects as freshers without working? No need to worry. I will share three options to build Experience as a fresher.
- Take up any Internship for Free/Paid [Goal is to get Experience, not salary]
- Take up any freelancing work from a platform like Upwork or through LinkedIn connections.
- Take up BEPEC Data Analytics Job Simulation Program with Build Experience to get real-time projects.
What are the Job roles after Data Analytics Course?
Once you have completed the training, you can apply for various Job titles like Data Analyst, Business Analyst, Decision Scientist, and Data Analyst in Marketing.
What is the future scope of Data Analytics?
The Data Analytics job profile is ancient. On average, 40000+ Job Openings every month across India. We can notice the massive amount of demand for Data Analysts. If we go back into history, we used to call them Business Intelligence Developers, Power BI Developers, Tableau Developers, SQL Developers, and various names, but every company need Data Analysts. Choosing a Data Analyst job profile is a perfect option for starting your career in IT.
Is Data Analytics Easy for Non-IT Students:
For Non-IT Students understanding Python is a bit complex. We need to give more time for practice and revision on those topics. For example, many of my Non-IT students made successful career transitions into Data Analytics.
How Do I Start a Career in Data Analytics?
First complete Job-Ready Data Analytics Course Syllabus like Python, Statistics, SQL, Power BI, Tableau & Excel. Once you finish with the Syllabus, you can start working on real-time projects, place them in your Resume, and start giving the interviews.
