Kashyap Dalal, Co-Founder and Chief Business Officer, Simplilearn

Kashyap is an internet entrepreneur with over 10 years of experience in building growth teams and hacking growth across functions. He is passionate about building high-performance teams and enjoys being actively involved in problem solving for business growth. At Simplilearn, Kashyap leads the Global consumer business and the Enterprise business in Europe, APAC and ME markets. Prior to Simplilearn, Kashyap was the Founder and CEO of Inkfruit.com, India’s first co-created products brand. Kashyap scaled the company to a successful exit in early 2013. He began his career with Unilever in Sales and Marketing. Kashyap is an alumnus of IIT Bombay and IIM Lucknow.

 

 

Back in 2012, the Harvard Business Review described the title of a data scientist as the ‘sexiest job of the 21st century.’ Sure enough, a decade later, the field of data science ranks among the most sought-after options for professionals across a variety of backgrounds. Typically the job of a data analyst today comes with handsome salaries, considerable job security, and a chance to work with cutting-edge technologies. According to Payscale, Data Analysts can earn an annual average salary of USD 61,881. In India, the average salary for a data analyst is about ₹439,260 per year.

What does the job of a data analyst entail? At a basic level, the job of a data analyst is to help businesses make sense of their data and enable better decision making. To achieve this, a data analyst must collect data, process it, and perform the required statistical analysis of data by applying the right algorithms and formulas.

Therefore, the day-to-day work of a data analyst typically consists of gathering and extracting numerical data; identifying trends and patterns within the data and interpreting the numbers. In addition, they are required to analyze market research and apply insights to guide decisions that drive the business. While the skills required to perform these tasks cannot be gained overnight, patience, hard work, and the right guidance can help steer you in the right direction. Also, a passion for numbers and a knack for extracting useful insights from processed data and presenting them in a useful format are valuable.

Preparing for a Career as a Successful Data Analyst

If you are keen to explore a career in data science, it is useful to master Microsoft Excel and build basic skills in SQL, web development, and data mapping. Of course, programming and software development skills are paramount. Some of the required skills include:

Programming languages

R and Python are perhaps the most popular languages used in data analytics. R is used extensively since it supports statistical computing and graphics. Specifically, skills in Dplyr acts as a bridge between R and SQL; ggplot2, a system that helps users build plots iteratively; and reshape2, which is based on meta and cast formats are important.

Python is also popular in data analytics for its ease of use, which makes it suitable for use in large projects. It’s also ideal for beginners, being one of the simplest programming languages. The presence of packages or libraries such as numpy, matplotlib, scipy, scikit-learn, ipython, ipython notebooks, pandas, anaconda, and seaborn makes Python immensely valuable in the world of data analytics.

Statistics and Mathematics

The knowledge of statistics is crucial – whether it is about forming data sets, or an understanding of basics such as mean, median, mode, standard deviation etc. In addition, familiarity with histograms, percentiles, probability, ANOVA, data chaining and distribution, correlation, causation etc. is also quite helpful.

Given that data analytics boils down to numbers, advanced knowledge of matrices and concepts such as linear algebra, relational algebra, CAP theorem, framing data, and series is extremely critical.

Machine Learning

Machine learning is quickly becoming central to data analytics. So, the ability to drive both supervised and unsupervised learning is crucial. While supervised learning models require the knowledge of tools such as logistic regression, decision trees, support vector machines, and Naive Bayes classification; unsupervised learning use Principal Component Analysis, Singular Value Decomposition, clustering algorithms, and Independent Component Analysis. Machine learning also uses reinforcement learning techniques where the knowledge of TD-Learning, Q-Learning, and genetic algorithms comes in handy for a data analyst.

Data Wrangling, Visualization, and Intuition

Data wrangling involves bringing together all the research data to form a single, cohesive whole. Data analysts must work with SQL and noSQL-based databases to enable this. Once this is done, the data analyst must also find ways to effectively communicate derived insights to stakeholders through visual encoding tools such as.ggplot, matplotlib, d3.js, and seaborne.

In addition, data intuition, which involves anticipating likely questions and blind spots and curating suitable responses is an important part of the role of a data analyst.

The field of data science today is quite vast, with a plethora of job profiles and opportunities available to anyone interested in building a career in this field. An aspiring professional could consider a role as a Business Intelligence Analyst, Data Analyst, Data Scientist, or Data Engineer. In addition, there are also opportunities to become a Quantitative Analyst, Data Analytics Consultant, Operations Analyst, Marketing Analyst, Project Manager, or IT Systems Analyst among other things.

If you’re interested in building a career in data science, there are several courses available to help build the necessary skill sets, ranging from certification programs to bootcamps that focus on teaching skills such as advanced analytics tools, data visualization tools, and programming tools. With the right training and aptitude in data science, the world is your oyster.

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