How to Become a Industry-Ready Data Science Professional?

Skills you must master!
1. data science Tool Kit
– Master Microsoft Excel – If you want to work with numbers, there is no better tool to start with than Microsoft Excel which is still the most popular tool around. Become comfortable with this tool.
– Explore Important Formulas and Functions – Master Excel features like pivot tables, quick charts, VLookUP, HLookUP, IFELSE, find and search, concatenate, SUM, AVG – you’ll need to have these handy when you’re working on real-world analytics projects.
– Create Charts and Visualization using MS Excel – There is no one size fits all chart and that’s why to create intuitive visualizations, it is imperative that you understand different types of charts and their usage. And Excel is the perfect tool to build advanced yet impactful charts for our analytics audience
– Get Familiar with MySQL – SQL is a must-have skill for every data science professional. Get yourself familiarized with one of the most widely used tools for data query and analysis. The majority of companies use SQL to make data-driven business decisions.
– Creating and updating reports in SQL – The majority of companies use SQL to make data-driven business decisions. Learn how to create and update records in SQL. This is one of the most frequent tasks.
– Performing Data Analysis using SQL – The data won’t speak for itself. In business scenarios, you’ll need to find answers by analyzing data and presenting them to stakeholders such as – Which cities brought in the maximum revenues? or What is the number of users who want to become data scientists or business analysts?
– Explore Python for Data Science – Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set. Mastering Python is essential to your skillset
– Important libraries and functions in Python – Python itself isn’t a machine learning language, the libraries and added functions like – Pandas, Numpy, Scikit-learn, Tensorflow make it very powerful.
– Reading file and manipulating data in python – Data is never found in a clean format. If you send dirty and untidy data into the model, the model will also return garbage, you must be well-versed with outlier treatment, missing value imputation.
– Working with data frames, lists, and dictionary – Python offers a range of options to store your data which are easy to understand. For better data handling you must them all. They are also frequently asked in interviews.
2. Data Exploration and Statistical Inference
– Working with pandas and other python libraries for data exploration – Pandas is amongst those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists. According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world.
– Use Matplotlib and Seaborn for data visualization – These are the most essential libraries that you’ll need to master at the beginning of your data science journey. These can never be avoided.
– Creating charts to visualize data and generate insights – No Data Science project can be started or ended without data visualization. A good data scientist is always a good storyteller. And a storyteller needs tools to visualize the facts and data.
– Univariate and Bivariate analysis using python – Getting a feel of the data is important. Univariate and Bivariate analysis help in uncovering patterns that lie hidden in the dataset and will help you in later stages as well.
– Perform Statistical Analysis on real-world datasets – Machine Learning coincides with statistical analysis and it is always better to perform basic statistical tests in the beginning to understand the quality of the dataset.
– Build and Validate hypotheses using statistical tests – Probably the most underrated yet the most important initial steps in a machine learning project. Any data science project always starts out as a hypothesis.