If you are a beginner trying to start your career as a data analyst, then getting certified with a data analyst certification is one of the best ways to have a firm base. This Excel to Python journey is two-fold where beginners gain a strong skill upgrade, allowing them to manipulate large data sets, conduct advanced analyses and unlock pathways for better career options.
Companies in this very data-driven world today still find it imperative to employ people who are capable of turning raw data into actionable insights. Excel has always been the dominant tool for data analysis, mainly because it is easy to use and people are familiar with it. Yet with increasing data volumes and more complex analyses, knowing programming languages like Python is a requirement.
Why Start With Excel?
For beginners, Excel is a very first thing introduced when starting to learn data analytics. Its GUI and formula-based approach also serve as a great way to learn basic data manipulation concepts such as cleaning, sorting, filtering, and some elementary statistical analysis. Excel is a key skill for most entry-level data analytics jobs, and it helps you to land an entry-level position.
Learning Excel helps you understand:
- Tables and Spreadsheets
- SUM, AVERAGE, VLOOKUP and Pivot Tables (Basic)
- Creating charts and graphs for data visualisation
- Simple automation using macros
It is great to work on less than medium datasets: I would say if we have a dataset between 1000–10,000 rows, you can do pretty much with Excel, but once the size goes above that, then it becomes complex.
The Power of Python in Data Analytics
Python, one of the most versatile programming languages used in the world today for data analytics because of its readability, richness of libraries and scalability. Python might seem intimidating as a new kid on the block if you are coming from Excel-land, but once you learn Python and Pandas, you open yourself to analysing complex datasets very quickly.
Without further delay, 5 reasons why you can not leave Python as an analytical language.
- Automation: Automating the redundant data cleaning and processing processes
- Advanced Analytics: Conduct statistical modelling, predictive analytics and machine learning.
- Big Data processing: large datasets that cannot be worked with in Excel.
- Being able to visualise your data is an essential quality; matplotlib and seaborn are the more powerful libraries for visualisation of data.
- Compatibility: Integrate with databases, web services, and more without any trouble.
Beginner’s Roadmap: Excel to Python
Step 1: Learn Excel Basics
Make sure you are comfortable with the basic Excel operations before going to Python. Learn to organise data, use formulas & create pivot tables. Learn Excel shortcuts and functions for a faster workflow.
Step 2: Learn Python Fundamentals
Learn Python basics like variables, data types, loops, conditionals and functions. Countless online resources and beginner-friendly tutorials make this step easy.
Step 3: Learn Data Handling Libraries
When we talk about the power of Python, it is only because of its libraries. Familiarise yourself with:
- Pandas: For working with data frames, in the way Excel tables are used.
- NumPy: To perform numerical operations and help in manipulating arrays.
- Matplotlib & Seaborn: For producing rich visualisations and plots.
Step 4: Data Cleaning and Preparation
Cleaning data is integral to analytics. This article will help you to practice importing datasets in Python, and cleaning them further by handling missing values, duplicate rows or columns & transformation of some variable format using Pandas.
Step 5: Experiment with Data Analysis Options
In this course, you will find out how to evaluate data with Python using precisely calculated summary data, detect trends and parse information by field. Segment data. Then start comparing your analysis results vs Excel outputs to boost confidence.
Step 6: Progress to Statistical + Predictive Analytics
After you get a feel for the basics, start to move on to more advanced techniques like hypothesis testing, regression analysis and machine learning algorithms in Python using libraries like Scikit-learn.
Step 7: Build Real-World Projects
Work on real-world business projects on sales forecasting, customer segmentation or churn prediction using your skills. Having them in your portfolio will make it much more likeable.
Simplilearn To Achieve Your Data Analytics Goals?
It is very important to have an ideal learning partner, and Simplilearn data analytics courses serve it all, starting from Excel basics to advanced Python analytics. Built with an industry-aligned curriculum, taught by expert instructors and enabled through real hands-on projects, Simplilearn provides you with the skills that hiring managers seek.
Some major data analyst certification programs are provided by:
- Excel for data analysis
- Python programming fundamentals
- Data visualisation techniques
- Statistical analysis and machine learning
- Hands-on/Machine Learning: Real World Case Study, Capstone Project.
After completing these courses, you can have technical knowledge as well as many more certifications, which are useful for getting a job.
Final Thoughts
For those starting in data analytics, the jump from Excel to Python is huge. You become well-versed in fundamentals like Excel and learn more modern and powerful tools such as Python, which are requisite in a work environment where data-related work has gained utmost importance. By staying on course with perseverance and the proper training that Simplilearn data analytics courses offer, you can tread this path confidently and emerge as a capable data analyst trained to take up any task.
