How a fresher can enhance data analytics skills?

Data analysts  are required to possess sharp technical skills complimented by strong industry knowledge. They require a complete understanding of relationships that exist among the organization’s various databases and data sources to be effective. As they acquire information using complex query statements and advanced database tools and techniques.

I've listed the skills a Data analysts are required to possess and the way an individual can go about enhancing them.

1. Machine learning

Machine learning combines data science, math, and software engineering, so it requires an extensive skill set to learn. Machine learning skills include computer science fundamentals, programming, probability and statistics, data modeling and evaluation, algorithms and libraries, and software engineering and system design.

Resources to learn machine learning: Kaggle has a community of data scientists and machine learning engineers who work together to publish datasets, build models, and compete to solve data science problems, which can be a good place to start.

2. Python

Python is a general-purpose, object-oriented programming language that runs on most operating systems, and has been one of the fastest-growing and most popular programming languages in recent years. It is also a powerful data and visualization tool, with a set of libraries that include a number specific to machine learning, including NumPy, SciPy, scikit-learn and Pandas, the report noted. Python is also the most frequently-mentioned skill found in data science job postings.

Resources to learn Python: You can practice Python on your own with resources like PyBites

3. R

R is an open-source statistical software package that simplifies the analysis of large data sets and includes features such as linear and non-linear modeling, clustering, and time-series analysis. R continues to grow in popularity, and, along with Python, is one of the most common skills listed in data science job postings.

R also allows data scientists to perform statistical and predictive analysis on real-time data, and then create interesting visuals to communicate that information to the business side,

Resources to learn R: R for Data Science is a good book resource that can be read online or in print.

4. SQL

SQL is a domain-specific programming language that makes retrieving data possible and gives data scientists a way to access and manipulate large amounts of information found in a relational database management system, according to the report. SQL commands can

capture and break down data, as well as edit database tables and indexes to improve accuracy. SQL skills are fundamental in the data science field.

Resources to learn SQL: SQL Fiddle is a free, interactive tool that lets users test and shares SQL queries in their browser.

5. Hadoop

Hadoop is a software framework that stores and processes large volumes of data across clusters of computing devices. It is flexible, scalable, and helps companies identify trends and predict outcomes to improve decision-making, the report said. While possible to get a data science job with limited Hadoop experience, a solid understanding of the framework is a strong selling point that can lead to more opportunities and better pay.

As there are hundreds of resources online but its highly unlikely that they are personalized to understand the individual queries faced by career aspirants in the realm of Data Science.

I would recommend visiting Board Infinity as we provide rigorous hybrid training programs for students and career aspirants. Learn in-demand skills & tools pertaining to the field of data science such as R, SAS, Python and implement concepts such as data exploration, regression models, hypothesis testing etc.