Generally, Data Analysis is a comprehensive process that involves taking unstructured information, inspecting it, cleansing, and then transforming key insights in a structured form. With advanced analytics, we use these data to further find specific patterns and draw conclusions that assist a large organization to make precise decisions for growing in a positive direction.
Data analysis nowadays is used across several businesses with a different approach, diverse techniques, and methods to help them make a precise decision for improvising efficiently. At Imarticus Learning, we help new age professionals to learn advanced analytics with dedicated courses to upskill them to match the corporate world requirements.
A simple data project follows this structure in the form as:
SQL for Extracting and transforming data,
Tableau for Data Visualisation & insights building as Hypothesis building,
R for Statistical Data Analysis with Bivariate & univariate analysis of variables, and
Python for Model development / Hypothesis testing.
Data analyst professionals deal with a very high amount of data daily. The first step is to learn SQL to analyze, extract, aggregate, and transform data for a more purposeful understanding. So, as a professional working in the data analysis field, SQL is the foremost priority to learn and manage data properly.
These datasets can have 1 million+ rows, here Tableau will work on visualizing data to bring insights or hypotheses. With Tableau, one can effectively track a million rows of information in data form to create useful insights.
R is another programming language used specifically for data analysis with the environment suited for statistical computing. One can also visualize data, blend data, build a statistical model, and perform complex transformations. R language is also preferred for developing statistical software so data analysts must have an understanding of its effectiveness.
Python is a general-purpose high-level programming language that most coders prefer to use. Python is used to develop algorithms from these large sets of data variables with scripts that make effective management to find relations and goals from the data itself. One must learn Python programming for building a sophisticated career in data scientists.
Although a data structure follows a specific path from SQL to Tableau to R to Python, still the goal and objective of the project define the purposeful use of that language. SQL helps us to query data properly; with Tableau, we learn to visualize data, R is better for exploration, while Python works better to get high production.
A well-organized course can help you to understand the right purpose for each of these languages precisely. Though an individual may not have expertise in each of these languages still, if you are opting for a career in Data analysis, you must understand the scope of SQL, Tableau, R, and Python to grow in the right direction.
At Imarticus Learning we offer several programs for professionals to learn advanced analytics and offer their expertise to the corporate world with definite preparations as well as courses to match their expectations.