Are you planning to learn Data Analytics? Do you want to take a formal learning path by registering for a training course in your city? Look no further. The post-graduate Data Analytics course in Kolkata is designed just for you. Learn about Data Analytics and the various techniques and tools to conduct analysis. Get familiar with the various nuances of data analysis, and how you can strengthen your critical thinking skills for Data Analytics jobs.
Data Analytics training courses collaborate with industry leaders to teach you about analytics, statistical techniques, data science technologies, programming languages and tools to help you succeed in your career.
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What is Data Analytics
Data Analytics is the process of analyzing raw data to find trends, anomalies and patterns for data-driven insights. It uses programming languages and libraries, SQL queries, statistical tools and techniques, machine learning algorithms to discover meaning in the data and create models to power decision-making.
Learning Data Analytics step by step
Learning analytics is not a two-day wonder. You must have formal learning and walk through the following steps to navigate the learning path.
1. Get familiar with programming languages
Get familiar with a couple of programming languages, beginning with Python. It is easy to grasp and flexible with the support of many built-in libraries like NumPy, Pandas and SciPy. Python helps to create and manage data structures quickly. The libraries offer many tools to manipulate and analyze complex datasets and data structures. Tools like the Scikit-Learn have advanced analytics capabilities with machine learning for sophisticated processing and modeling. Python is also used to write data analysis algorithms for direct integration into BI tools, making it the most useful language to learn.
R is another preferred language of data analysts and scientists and can handle vast data. Tableau and MS Excel are some other software that you can explore.
2. Learn SQL
Structured Query Language (SQL) is a powerful Query Language used to store data or fetch the data from Relational Database Management Systems (RDBMS). SQL language consists of actions, which execute to assemble data, like Select, Update, Delete, Add, Modify, Alter, and other clauses.
Data Analytics uses SQL for performing actions like updating the data or deleting it, for data preparation and wrangling, and using Big Data tools. For advanced analysis, standard SQL tools are a must-know. SQL knowledge is required for accessing data available in Relational Databases like Oracle, Microsoft SQL, or MySQL.
3. Develop knowledge of Statistics
Knowledge of statistics and probability is a part of the Data Analytics learning curve. Statistics is a core competency area in Data Analytics for mining large data sets and extracting insights. It is a fundamental tool for gathering and analyzing large amounts of structured and unstructured data.
To become a data analyst, you must understand statistical concepts, have the ability to interpret statistical results, and report on the findings.
4. Learn Data Cleaning
Data cleaning is a critical skill set for Data Analytics. It refers to the process of identifying and removing the unwanted, inaccurate, incomplete, or missing values from the dataset. This data is usually considered unnecessary for Data Analytics and may give inaccurate outcomes. However, more than the removal of data, data cleansing is about enhancing the dataset’s accuracy and includes correcting spelling and syntax errors, fixing empty fields, discovering duplicate data points and missing codes.
Data cleaning ensures that the data used is clean and produces fairly accurate results. So learning this step is a necessary part of learning Data Analytics.
5. Learn Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is an approach to analyzing data where you take a bird’s eye view to understand it. It refers to the process of investigation where the analyst tries to get the feel of the data using their judgment. He/she determines the important elements in the data, discovers patterns and anomalies, and tests hypotheses using graphical and statistical methods like data visualization and data manipulation.
As it is usually the first step in data analysis before the application of any formal statistical techniques or modeling, EDA forms part of the initial learning curve. The purpose of learning EDA is to gain maximum insight into the data set, discover missing values, create a list of outliers or anomalies, and identify the most influential variables.
6. Learn Machine Learning Algorithms
Machine learning is a method of data analysis for automating analytical model building. “It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”
The phenomenal increase in the volume of data processed, varieties of data, affordable data storage, and cheaper computational processing has made the automation of data modeling possible on a large scale. Machine learning uses an iterative method to learn from data and can be easily automated until a pattern is found.
It forms a critical step in the Data Analytics life cycle where you need to build multiple models using machine learning algorithms for prediction and optimal solutions to problems.
7. Get hands-on
Once you have navigated the above steps, it is time to get hands-on experience. Get hold of free to download and use data sets, on which you can practice. Work on projects. Start with analyzing the data and implementing the techniques you have mastered. Discover various ways to solve a problem. Maybe even participate in online competitions to sharpen your skills.
8. Create a portfolio
Add a portfolio to your Data Analytics Certification to land a good job. It will help you succeed in your Data Analytics job interview. A Data Analytics portfolio helps you stand out from the rest of the crowd. Your portfolio is the mirror to the world, showing your critical thinking and problem-solving skills.
What is a Data Analytics portfolio? It features your capstone project, your internships, projects you have worked on, your GitHub link, with details of any project you have worked on, any papers you may have published, or any competition you have aced.
Why create a portfolio? Companies look for candidates with real-world experience, and it is often difficult for a fresher to compete with experienced applicants. Creating a portfolio showcases the skills of the applicant and places the data analyst wannabe on an equal footing when applying for a job.
9. Check out online blogs and resources
Today, you have many websites and online resources to add to your knowledge base and Data Analytics techniques. With their self-paced learning and knowledge content, they are a good place to start. From links to data sets and analytical techniques, problems to solve, and industry case studies, these blogs and resources are a mine of information.
10. Get familiar with the industry
Read up on industry news and learn about case studies. Understand different industry domains where Data Analytics finds prolific use. Include a variety of domains in your projects.
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