Jobs & Career

Starting Your Career As A Data Analyst

The Data Analyst job was named the ‘hottest job of the 21st century’ by Harvard Business Review. This field has grown a lot and has become one of the most sought-after professions in the past few years.

A data analyst is responsible for collecting, processing, and performing statistical analysis of data for businesses use. This data will be useful in taking many important decisions for an organization. A Data analyst also helps in prioritizing the raw data collected to make the process smooth. For this purpose, Data Analysts use specific formulas and algorithms.

If you like to work with numbers and algebraic functions then you will surely do great as a Data analyst. In this article, we will try to give an overview of the things that can help you become successful as a Data analyst.

Data Analysts should know how to work with Microsoft Excel. Although this is a very basic skill and the non-structured data on Excel is of not any importance. Data analysts should have basic Data mapping, SQL, and web development skills. They also can find patterns in large data sets and derive actionable insights from processed data.

Data analysis covers a wide domain, sometimes it deals with statistics and higher mathematics,  and sometimes with programming and software development. This is the reason that this field promises a great number of jobs and success.

Programming languages required for Data analytics

R and Python are among the extensively used programming languages for data analysis. These are the languages that a Data Analyst should learn. The programming language R supports statistical computing and graphics. On the other hand, Python is easy to use in large projects.

Programming with R:

The programming language R requires you to know some important topics if you want to become an expert and do your work efficiently. Three important areas are listed here.

Dplyr combines R and SQL. Along with the translation of codes in SQL language, it is capable of working with both types of data.

ggplot2 helps in building editable plots. These plots can be edited according to the need based on the graphics. Two Ggplot2 sub-systems are being used these days and they are ggally (helps prepare network plots), and ggpairs (matrix).

reshape2 has two formats that are meta and cast. The meta format converts data from broad format data to long format data. While the casting format does the opposite.

Programming with Python:

Python is mainly preferred by beginners as it is very easy to understand. The Python programming language has the packages or libraries to provide you a head start in the data analyst world. Some popular and highly used libraries are numpy, pandas, matplotlib, scipy, scikit-learn, ipython, ipython notebooks, anaconda, and seaborn.

Other required skills:

Statistics

Data and statistics are so related to each other that whenever you talk about Data, statistics will automatically come into the picture. Statistical skills such as forming data sets, mean, median, mode, SD, and other variables; histograms, percentiles, probability, ANOVA, correlation, causation are the important skills for a Data Analyst should have.

Mathematics

Data analytics is called a game of numbers so you must be great at working with numbers. Data Analysts should have advanced knowledge of matrices, linear and relational algebra; CAP theorem, framing data, and series.

Machine learning

Machine learning is originally a combination of multivariable calculus and linear algebra, along with statistics. This makes it the most powerful skill for Data analytics. There is one more benefit that makes is more useful that in machine learning you don’t have to get involved in any of the machine-learning algorithms for upgrading your skills. Supervised, unsupervised, and reinforcement are three types of machine learning being used these days.

Data Wrangling

In Data wrangling, all the research data is taken together to form a single and united whole. In this process, raw data is converted to structured, logical sets making them usable for future purposes. The required tools for this process are SQL and non-SQL-based databases.

Communication and Data Visualization

Data analysts are not only responsible for data interpretation and reporting but they also have to communicate important information to all the stakeholders involved. In this process, they use visual encoding tools, like asggplot, matplotlib, d3.js, and seaborne.

Roles and responsibilities of Data analysts

  • Interpret the numbers
  • Analyze market research
  • Gather and extract numerical data
  • Find important trends, patterns, and algorithms within the data

Data analysts mostly deal with numbers so you need to be handy with numbers. The ability to extract useful information from processed data along with the presentation of this data accurately are the two basic things for becoming a successful Data Analyst. As we all know that no skills can be learned overnight. You will have to make a good plan and follow it step by step with patience and hard work.

With the help of the right guidance, you can achieve your dreams and accelerate your career trajectory. If you want to start your career in this field then you should have a certificate in data analysis.

Becoming a certified Data Analyst will help you get the attention of the employer and reduce competition. If you are ready to prepare the 1D0-622 exam that is for the Data Analyst certification, then uCertify is the right place for you. we’re excited to tell you that we have purses equipped with the best learning resources to help you pass the certification exam with flying colors.

This recognized and appreciated course will teach students all the basic and advanced concepts covered in data analysis. Students will gain knowledge on topics like the fundamentals of data analysis, big data, working with data sources, tools for capturing and analyzing data; and analyzing and reporting data. Check out our course and become a Data Analyst!

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