Hey Data Enthusiast, if you are thinking of starting your career in Data Analytics, this blog post will help you give an overview of essential tools you need to know in Data Analytics. After searching numerous job posts for data analytics, I found that these skills are most demanded in the data analytics role, and learning them can harness your analytical ability in the data world.

1. Excel

Excel is the most widely used tool in data analytics and it’s my favourite tool as well. It’s known for its versatility and ease of use. It has evolved from a simple calculation tool to a robust platform for data management, analysis, and data visualization.

Key Features:

• Formulas and Functions: Excel has various built-in functions that allow users to perform calculations, analyze the data, and automate the different manual calculations. Most used functions like VLOOKUP(), SUMIFS(), IF(), and AVERAGEIFS(), etc. are essential in data analysis.
• Data Visualization: Excel has a variety of Chart types, Pivot Tables, and Conditional formatting options to create interpreting visualization to make data look easier and presentable.
• Data Cleaning and Manipulation: Excel has different filtering, and sorting options that can help to clean, validate, and organize the data.

User Cases:

• Excel is widely used in Financial Modelling for accounting and financial analysis in building budgets and forecasting.
• Many small Businesses use Excel to track sales and inventories.

Limitations:

• Excel has a limit of 1,048,576 rows and 16,384 columns per worksheet. So, for large datasets, it can be a constraint. Also, when the dataset grows, Excel can slow down and take more time to load.
• Manual Data Entry in Excel can be prone to human error and it can compromise the data integrity.

2. Power BI:

Power BI is the leading Business Analytics tool from Microsoft which enables users to visualize the data and share valuable insights to business stakeholders. It’s amongst data analytical tools which have gained popularity quickly. It can combine the data from various sources, transform it into actionable insights, and help to make business decisions.

Key Features:

• Data Connectivity: Power BI can connect various data sources like Excel Workbook, SQL Databases, Web Data, and Cloud Services like Azure, etc.
• Custom Visualization: Power BI has different interactive visualizations like Bar charts, Pie charts, etc. to present the data with the best suite.
• Real-Time Data Monitoring: Users can set the created dashboards which updates in real-time can allows them to share immediate insights with the business.
• Language Querying: Users can also ask questions to get actionable insights.

User Cases:

• Power BI is used in various industries like Sales and Marketing, Finance, Human Resource Management, Operations and Supply chain management, Healthcare, Education, Customer Service, etc to analyze, track, and forecast the data.

Limitations:

• Power BI Pro has a maximum data size of 1GB per dataset while Power BI Premium allows largest datasets up to 400 GB. Hence for extensive Data Analysis this limitations can be restrictive.
• As reports become more complex and data sources increase in size then performance can suffer and lead to slow down the loading of data.
• Sharing the Power BI reports requires the Power BI Pro license, which can add cost.

3. SQL

SQL stands for Structured Query Language. It is a programming language that is used for managing and manipulating relational databases. It’s one of the most important Data Analytical tools for Data Analysts and Data Scientists. It allows users to perform various operations like Querying the data, Inserting the records, updating the records, and deleting the records. Its syntax is pretty straightforward and allows users to write queries that can quickly retrieve the data.

Key Features:

• Data Retrieval: SQL is useful for retrieving data by using powerful queries. Users can filter, sort, and aggregate the data to extract meaningful data.
• Data Manipulation: SQL allows users to create, read, update, and delete the records in databases which helps users to manage the data efficiently.
• Join Operations: Users can combine multiple tables with join operations in SQL to analyze the data easily.

User Cases:

• SQL is widely used in Data Warehousing solutions to organize and query data efficiently.
• Many developers use SQL to interact with and manage the databases which supports to web applications.
• Many Organizations use SQL to analyze sales data, customer information, and financial records which helps in decision-making.

Limitations:

• Sometimes beginners may find it challenging to write complex queries such as multiple joins,

Subqueries.

• Different database systems such as MYSQL, PostgreSQL, and SQL Server use variations of SQL which can lead to compatibility issues and a need to learn the different syntaxes.

4. Python

Python is a versatile and powerful programming language that became a favorite in the data analytics, data science, and machine learning field. It’s known for its readability and simplicity. Python has a wide range of libraries and frameworks such as Pandas, Numpy, and Matplotilb which help in data manipulation, analysis, and visualization.

Key Features:

• Data Manipulation: Python is a powerful tool for data cleaning and transformation. Libraries like panda enable users to clean, transform the data and analyse structured data.
• Numerical Computation: Libraries like Numpy offer a collection of mathematical functions, arrays, and matrices to operate on data structures.
• Data Visualization: Libraries such as Matplotlib offer a wide variety of static, animated, and interactive visualizations.
• Machine Learning: Libraries such as Scikit-learn and TensorFlow make Python helpful for building machine learning models.

User Cases:

• Python is useful to automate extracting the data from the web. it also allows user to gather and analyze the large volume of data.
• Python helps analysts perform many data transformations, analysis, and generate reports for business decisions.
• In Machine Learning and AI, Many data scientists use Python to develop algorithms that predict the outcome based on historical data.

Limitations:

• Python has generally slower execution than other compiled languages such as C or C++. Because it’s interpreted language.
• Python can consume more memory than another programming language which can be a constraint.

Conclusion:

As you start your data journey, understating these essential tools can give you a strong foundation. Each tool has its strengths and limitations, so consider your specific needs and goals when deciding to start. If you have an interest in data analytics, then start today. I hope this blog can help you to give you an overview of the tools.