If you really want to make your career in data science then you should always need to know about the basics and advance of data science to do data analysis.

 

Learning these skill’s also needs the knowledge of mathematics like statistics, mean, median, mode, percentiles and etc.

 

You always need to the what, why, when, how of the data science; and also, why we need this in our daily life to solve our problems.

 

You also should make the use of AI to solve and do the data science problems.

 

Data Science is acknowledged as the sexiest job of 21^st century due to its high pay packages, career growth and high demand in the market; because now every organization wants to use data science to take their business decisions.

 

1. 📊 Excel.

 

If you are entering into this field then you need to learn the basics of data science and this you can start to learn with the help of MS Excel software.

 

Excel is a powerful tool of Microsoft which is able to perform every type of data analysis.

 

Use this tool to start your journey in data science, using this software for initial learning of data science can boost your morale and confidence.

 

This tool will help you to learn about the basics of data analysis / analytics and makes your understanding more clear towards doing data analysis operations.

 

This tool is specialized for non technical users; who find difficulties in going backend / technical.

 

Pros	Cons
Easy to learn and widely used	Limited scalability with very large datasets
Strong formulas and pivot tables	Not ideal for advanced analytics
Integrates well with other Microsoft tools	Collaboration can be clunky compared to cloud BI
Flexible for quick analysis	Error prone with manual data entry
Cost effective (often bundled with Office)	Weak visualization compared to BI tools

 

2. 📈 Microsoft Power BI.

 

Now Power BI is another tool by Microsoft, this is a data visualization software which is widely used by organization to visualize their data analytics.

 

This software helps to quickly interpret the data with its visualization power.

 

To take quick decisions; data visualization plays an important role such as predictive analysis.

 

Its user friendly but the usages are limited; means does not support large data sets.

 

Pros	Cons
User friendly drag and drop dashboards	Requires training for complex data modelling
Affordable pricing	Performance issues with very large datasets
Seamless integration with Microsoft ecosystem	Less customizable visuals than Tableau
Strong data connectivity (cloud + local)	Limited advanced statistical features
Built in AI and natural language queries	Governance setup can be complex

 

3. 📉 Tableau.

 

Tableau is also an data visualization software with its advanced features and capability to handle large data sets and is used for real time data analysis.

 

Its also a user friendly software used by many corporates and agencies to run their data related operations.

 

Tableau is compatible with every type of data sets; some features like drag & drop process makes tableau easy to handle.

 

Pros	Cons
Best in-class visualizations	Higher licensing costs
Strong community and support	Steeper learning curve for beginners
Connects to many data sources	Limited integration with Microsoft tools
Real-time interactive dashboards	Performance issues with massive datasets
Advanced analytics with R/Python integration	Requires optimization for enterprise scale

 

4. 🐍 Data Science Programing Languages like Python / R.

 

If you want to go core in data analytics; then you need to learn the advanced programing languages for data handling like python libraries, and R languages to do data analysis.

 

Doing data analysis with the help of programing language helps you go beyond the basic data analysis; you can apply and perform advance level of data analysis strategies; if you carry perfect command over programing languages.

 

You can prepare you own customizable machine learning data models with help of these programing languages to do real time data analysis.

 

Pros	Cons
Python: versatile, huge ecosystem	Python slower than compiled languages
R: excellent for statistics and visualization	R less suited for production systems
Open source and widely supported	Steeper learning curve for beginners
Integration with ML/AI libraries	Requires coding knowledge
Strong community and resources	Can be fragmented across packages

 

5. 🗄️ SQL for database management.

 

SQL stands for ( Structured Query Language ) which allow us to handle large volume of data sets; it allows us to perform multiple types of queries and helps retrieve data quickly according to demand.

 

It is an standardized form of data base management; where you can store and can manipulate data; it is supported and compatible with various database system such as MySQL, PostgreSQL, Oracle, SQL Server.

 

SQL language is the most trusted way of collecting and organizing data which we collect from various other platforms and is free and opensource.

 

Pros	Cons
Standard language across databases	Limited for advanced analytics
Efficient querying of large datasets	Requires optimization for performance
Easy to learn basics	Complex queries can be hard to debug
Strong integration with BI tools	Not suitable for unstructured data
Essential skill for data professionals	Vendor specific variations (MySQL vs SQL Server)

 

6. 🤖 Machine Learning AI / ML.

 

When we collect data from various other online platform such as search engines / websites / social media, etc.

 

Need to clean / modify / structure that data to make it ready for the future usage.

 

There is always a need to train that data to do complex task and also to do future predictions.

 

Apply different types of machine learning models on processed data to perform specific task like automation and updating real time data; such as customer purchase prediction or to identify the user behavior on different segments.

 

Pros	Cons
Automates decision making	Requires large amounts of data
Improves predictions and personalization	Models can be biased if data is biased
Continuously improves with new data	Complex to explain (black box models)
Wide applications across industries	High computational cost
Drives innovation (healthcare, finance, retail)	Needs skilled professionals to implement

 

7. 📊 Dashboarding / Automation for real time analysis.

 

If you want your data to perform updated real time data analysis then you need to perform task related to Dashboarding / Automation which is actually an updated insights derived from updated data.

 

By this you save you time and effort by not manually updating the data, the data automatically fetches the current data by itself.

 

That’s why Dashboarding / Automation is needed to be applied on large volumes of datasets which are not easy to handle and maintain manually.

 

Pros	Cons
Provides instant insights	Requires reliable data pipelines
Improves decision making speed	Can overwhelm users with too much data
Automates repetitive reporting	Setup can be complex
Enhances collaboration	Maintenance needed for accuracy
Integrates with BI tools	Costly for enterprise grade automation

 

8. 🗂️ Management Information systems ( MIS ).

 

An MIS is a skill which deals in data storage / handling / cleaning / structuring / Data manipulation and etc.

 

This skill requires to learn; that how to maintain data, which always deals in large datasets.

 

In this the individual is needed to learn data security and problems related to data server management that how to maintain a data server / data centres.

 

All together the individual is responsible for maintaining and handling data servers with help of technical requirements like knowledge of SQL and other programming languages needed in data server management.

 

Pros	Cons
Centralizes organizational data	Implementation can be expensive
Supports decision making	May require customization per industry
Improves efficiency and reporting	Can be rigid compared to modern BI tools
Enhances communication across departments	Training required for adoption
Provides historical and trend analysis	Limited advanced analytics compared to AI

 

9. 🚀 Model Deployments.

 

Now, when we have trained data and is applicable for deployment / usage in form of gadgets, tools, software or doing predictions.

 

Then we need to deploy or embed machine learning codes on websites / Apps / online Software’s to act as like an agent to do or perform the task for users.

 

This makes the online machine learning operations to perform automatically; by actually avoiding any type of extra human efforts repeatedly.

 

Model deployment uses platforms like Microsoft Azure, TensorFlow, Amazon ( AWS ) and etc.

 

Pros	Cons
Moves ML models into real world use	Requires infrastructure setup
Enables real time predictions	Monitoring and retraining needed
Scales across applications	Complex for beginners
Integrates with APIs and apps	Risk of model drift over time
Supports automation and business impact	Security and compliance challenges

 

10. 🌐📱 Website / App’s, machine learning & AI model Integrations.

 

Now when you have deployed ML / AI model in Apps, online software’s, websites; then you always need to check and perform its usage and ensure that whether it is working correctly or not.

 

Because with the help of machine learning models we can make tools and gadget like, calculator, currency converter, file converter, generative AI tools / browser extensions, gadgets and many other different types of applications to perform real time task for users / audience.

 

Pros	Cons
Brings AI directly to end users	Integration complexity
Enhances user experience (recommendations, personalization)	Requires strong backend infrastructure
Supports mobile/web scalability	Can increase latency if not optimized
Enables innovative products	Higher development costs
Competitive advantage in digital markets	Ongoing maintenance and monitoring

 

✅ Conclusion.

 

If you are a student who is looking to make career in data science; remember you at least need to master the above described skills to make your career in this field.

 

Or if you are a working professional such as doing freelancing or job; can help you for your career growth if master the above data science skill.

 

Learning these skill helps you to learn lucratively because of its high demand for data science professionals and its positions are increasing annually.

 

You can find lot of data science freelancing work online; which can help you to earn your second salary in free time.