How to Conquer the Territory of Data Science
It is extremely important for a data scientist to reshape and refine the datasets into usable datasets, which can be leveraged for analytics. In this article we will look at the important aspect of data preparation and analysis
In the field of data science, handling of data is one of the crucial tasks. Data handling is the driving factor for the data science process and clean data is important to build successful machine learning models as it enhances the performance and accuracy of the model. Data handling has three important measures pre-processing, analyzing and interpreting into meaningful insights. Right pre-processing methods with proper analyzing tools always results in meaningful insights.
As a data expert, it’s very important to understand the problem statement and relate its relevancy with the available dataset. Understanding of the problem statement and apt knowledge of differentiating between various definitions lays a strong foundation for Data Scientists.
Why every Data Scientist should know SQL?
SQL is the first and most reliable weapon of a Data Expert. As we have already discussed the basics of SQL in the previous article, here we will take it one step forward.
There are some key concepts one needs to master to unfold the other mysteries of data science. We’ll discuss them right here -
SQL commands: All the commands used in SQL to perform various operation fall into a separate category. The categories are as follows:
- Data Definition Language (DDL): The DDL commands such as create, drop, alter and truncate is used for creating, dropping, altering and modifying the structure of database objects.
- Data Manipulation Language (DML): The DML commands such as insert, update and delete are used for inserting, updating and deleting the structure of database objects.
- Data Control Language (DCL): The DCL commands such as grant and revoke are used for providing security to database objects.
- Data Query Language (DQL): The DQL command such as select is used for retrieving data from the database.
- Transaction Control Language (TCL): TCL commands such as commit, rollback and savepoint are used for managing transactions in the database.
Understanding of Advanced SQL: After gaining insights on the basics of SQL, it is time to dive deeper into another concept which is Advanced SQL. In this part, we will learn about various other keywords and concepts such as
- UNION (combine the results of two or more SELECT statements without returning any duplicate rows)
- UNION ALL(combine the results of two SELECT statements including duplicate rows.),
- INTERSECT(returns only common rows returned by the two SELECT statements),
- MINUS(displays the rows which are present in the first query but absent in the second query, with no duplicates and data arranged in ascending order by default)
- LIMIT(fetch the limited number of records)
- TOP(fetch a TOP N number or X percent records from a table)
- CASE(selects one sequence of statements to execute)
- DECODE(compares expression against each search value in order)
- AUTO-INCREMENT(used for auto-incrementing a value of a field in the table)
etc. in order to create advanced reports and perform complex pattern matching.
Knowledge of Joins: The different types of Joins are -
- INNER Join: This join selects all the records with matching values in both the tables.
- FULL Join: This join selects all the records either from the right table or left.
- LEFT Join: This join selects records left-most table along with the matching records from the right table.
- RIGHT Join: This join selects records from the right-most table along with the matching records from the left table.
As we discussed that data pre-processing precedes with Data Analysis. SQL helps to clean the data and get the desired data frame from a given dataset and also allows us to analyze the data at a certain extent. However, for getting a better picture of a dataset we need to opt for Pandas.
Pandas: Pandas library is one the most useful data munging tool, but before jumping directly into Pandas it’s a pre-requisite to learning coding conventions of Python and have some familiarity with SQL concepts.
One could start off by installing “Anaconda Python” and Jupyter notebook. There are ample of tutorials available on the Internet for Pandas.
One important thing to note, especially for the beginners, always try to go for video tutorials. It helps to create a better understanding of the topic.
So, here we discussed a short and simple roadmap for data analysis. In the next article, we will learn the next steps of data analysis and data interpretation.
Data Science as a career is a great option that is enticing as well as rewarding. However, it’s a challenging role too — no wonder it requires attention and patience to learn. For long-term success, you need to really build a strong foundation in this domain. All the best!
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