Are you ready to upskill and future-proof your career in the rapidly evolving job market? In today's data-driven world, the demand for professionals with expertise in Data Analytics Tools is on the rise.
If you're interested in upskilling with Data Analytics Tools but are unsure where to start, don't worry! WIZR can help guide you towards the best courses and programmes that align with your goals.
What is Data Analytics?
Data Analytics is the process of analysing data sets and extracting valuable insights to support business decisions. It is widely used across industries such as healthcare, finance, marketing, and technology.
Excel as a Data Analytics Tool
Excel is a versatile tool for data analysis that most freshers are already familiar with. With its simple interface and powerful features, it is an ideal choice for beginners in data analytics.
You can use Excel to create charts, graphs, and pivot tables to analyse large datasets.
Basic features of Excel for data analysis include:
- VLOOKUP: To search for specific information within a dataset quickly
- Pivot Tables: To filter and summarise large amounts of data with ease
- Macros: To automate repetitive tasks by recording your actions as a script that can be run again later
Some real-world examples of how these functions are used in different industries include sales forecasting, inventory management, financial analysis, and social media marketing.
Advanced features of Excel for Data Analysis
Excel offers advanced features such as Power Query and Power Pivot add-ins that allow you to analyse large datasets more efficiently.
- Power Query enables you to connect with external data sources, such as web pages or databases, easily.
- Power Pivot adds powerful analytical capabilities by allowing you to create relationships between tables and perform complex calculations on large datasets.
To help you make an informed decision about using Excel as your primary tool for Data Analytics, here's a list of the key advantages and disadvantages below.
Python for Data Analytics
Python is one of the most popular programming languages used in Data Analytics Tools. Its ease-of-use, flexibility, and powerful libraries have made it a top choice among data analysts.
Libraries such as Pandas, NumPy, and Matplotlib make it easy to clean, analyse, and visualise large datasets.
Basic features of Python for data analysis include:
- Pandas: Helps in manipulating the dataset using DataFrames
- NumPy: Makes working on arrays easier
- Matplotlib: Provides plotting functions
Some real-world examples include image processing, web development, text mining, and natural language processing.
Python is also used in machine learning tasks with libraries like Scikit-Learn and TensorFlow. These libraries are useful for building predictive models by training algorithms on historical data. Python's memory management makes it suitable for handling big data as well.
However, there are some advantages and disadvantages to using Python as your primary tool for Data analytics, as shown below.
R Programming Language for Data Analytics
R is a programming language that was designed specifically for data analytics. It is known for its powerful statistical analysis capabilities and is widely used in academia and the healthcare industry.
Basic features of R for data analysis include popular libraries such as:
- Dplyr: Helps in manipulating datasets using DataFrames
- Ggplot2: Provides functions to produce high-quality graphs.
- Tidyr: Assists in creating well-structured data.
Some real-world examples of how these libraries are used in different industries include credit risk modelling, fraud detection, biostatistics, and clinical trials.
R is also useful in machine learning tasks with libraries like Caret and RandomForest. These libraries are useful for building predictive models by training algorithms on historical data. You can use R for big data processing too, with several packages like SparkR.
However, there are some advantages and disadvantages to using R as your primary tool for Data analytics, as shown below.
SAS for Data Analytics
SAS is a software suite developed specifically for data management and analytics. It is widely used in the finance, healthcare, and retail industries. Popular procedures in SAS include PROC SQL and PROC MEANS.
Basic features of SAS for data analysis include:
- PROC SQL: Helps in querying databases
- PROC MEANS: Summarises statistics on your dataset
Real-world examples of how these procedures are used in different industries include financial modelling, fraud detection, and market research.
SAS is useful for machine learning tasks with libraries like EM and Enterprise Miner. These libraries are useful for building predictive models by training algorithms on historical data. You can use SAS for big data processing too, with tools like Hadoop integration.
However, there are some advantages and disadvantages to using SAS as your primary tool for Data analytics, as shown below.
Career Growth Opportunities
The demand for skilled data analysts, business analysts, and data scientists is on the rise in India. These job roles require professionals who can use various tools and techniques for data analysis, interpretation, and presentation.
Salaries in Data Analytics
A data analyst can expect to earn around ₹5-6 lakhs annually, while a business analyst can earn up to ₹9-10 lakhs per annum. Data scientists with 4-5 years of experience in high-growth industries can make an average annual data analyst salary of ₹20-25 lakhs.
A Future in Data Analytics
Data analytics is a rapidly growing field with endless opportunities for fresh graduates and young professionals in India.
Various job roles require different levels of education or certification options. WiZR is an ed-tech platform that guides young students across India to pick the right course. Enrol now to enhance your career in data analytics!
FAQs
1. What are the main categories in data analytics?
Data analytics consists of three primary categories: descriptive analytics, predictive analytics, and prescriptive analytics.
2. Should I learn R or Python first?
It depends on your career goals and interests. Python has more extensive applications, while R is commonly used in academia and research.
3. Which visualisation tool is best for data analytics?
Tableau, Power BI, and QlikView are considered among the best data visualisation tools in the industry.
4. Which is the highest-paying data analysis job?
The highest-paying jobs in data analytics are those of a Data Scientist or Machine Learning Engineer in India.
