Before jumping to Python let us have a look at data science.
We all can agree that data science has become one of the most famous and high-paying jobs with minimum requirements for any fresher from any background whether it is a BA, MA, MBA, B.Tech, BBA, BCA, etc from the last 5-6 years. This was also the period when the world faced a pandemic but IT was the sector that was making profits. This is also one of the reasons why companies pay handsome salaries to fresher candidates for data science profiles.
Now the question arises…..
How to start a career in this Field?
It’s been approx 5-6 years since the market experienced a high demand in this field. Companies are hiring data analysts in huge numbers from time to time and still, there is a significant gap in this industry and companies are in a continuous chase of highly skilled data analysts in the market.
But being a fresher, the problem arises from where we can pursue this course, improve our skills, improve our resume, and become a potential candidate to be hired by the big giants. As we discussed, this skill has been in demand for the last 5-6 years, and there are very few colleges that have started a degree program in this field but the results are not enough.
Here, comes the role of data science training institutes to help the aspirants in creating and shape their careers in the data science field. These institutes have become a bridge to fulfill the demand-supply gap in this field but still, the gap is so huge that it is increasing day by day. So whether you are a working professional or not, you are from a technical background or not, there is nothing that can stop you from creating your career in this field.
If you wish to learn more about the Data Science field, Download this brochure
The Most Important Aspect that needs to be taken care of while choosing an institute is that…..
What are they teaching us and Why?“
Data Science is a very huge field that demands many tools and technologies like a programming language like Python, libraries like Numpy and Pandas, interactive dashboards like PowerBI, SQL for data migration, PySpark for big-data analytics, DataBricks, etc. So all these tools that we must have hands-on hold a specific reason and requirement in this particular field.
So in this blog, we are going to cover…
The necessary topics that we need to cover in Python
We all know that Python is a general programming language which means Python can be used for web development, software development, data science, game development, etc but we have to cover Python for the data science field which simply means we do not need to cover python for the game, software or web development.
Topics in Python that are necessary to learn for Data Science
Data Types: The first topic that we need to cover in Python is Data Types. In simple words, we can conclude that every data is important but Python has categorized all the data into 8 types(4 basics +4 advanced). Firstly we can start with the basics which are Integer(int), Float(Float), String(str), and Boolean(bool).
Variables: After data types, we need to cover the concept of variables. We can relate the concept of variables to the containers that we use in our daily lives to store our stuff safely and securely so that we do not need to memorize all our data and we can access them easily whenever we want.
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Operators: Operators are the collection of symbols and special keywords that help us to perform calculations, manipulations, and comparisons on the data. They are majorly categorized into 4 types arithmetic, assignment, comparison, and logical.
User Input & Typecasting: Right now whatever we have covered works on the data from a developer’s point of view but the program can not interact with the user which means it will generate the same output again and again if the developer does not interfere. So what we need here is a solution with the help of which a user can interact with the program, provide his/her own data, and the program generates output on behalf of that data.
Python also has the concept of typecasting which helps the programmers to change the type of data from one to another as per their requirement. This feature helps the programmer from many different perspectives in using particular data in different forms.
Strings: A character or a collection of characters enclosed within single (‘ ‘), double (” “) or triple(”’ ”’) quotes is a string. It is an important type of data in Python to learn as it will also help us a lot in data analytics operations also known as string manipulation. The commands that help us in doing operations on string data type are called string methods.
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Branching(Conditional Statements & Loops): There are times when we have to perform a task according to a particular condition otherwise the task gets changed. This scenario is covered under conditional statements where we get to learnlike how to handle multiple tasks depending upon multiple conditions at once.
Whenever we have to perform a particular task again and again for a particular period of time or numbers, then it is called loop. Loops help us to automate performing our same task again and again till our goal is achieved. It helps us in such a way that we do not have to instruct the computer again and again for a particular kind of task.
The backend processing of conditional statements and loops create a branching structure that’s why we call them branching together.
List: Now that we have covered data type, operators, and branching now is the time to work on huge amount of data so in order to handle huge amount of data we have a member of collection family which is list. A list is a collection of items stored in a single variable. This is also an important topic in terms of data analytics. The commands which we use to perform list manipulations are called as list methods.
Set: We have one more data type that is set which helps us in handling huge amount of data. It is also a member of collection family. A set and a list are a collection items stored in a single variable but both of them possess different properties and as per our requirement we prefer between list and set to perform our operations.
Tuple: One more member of the collection family is Tuple. Again tuple is a collection of items stored in a single variable. If you have understood list, the tuple is going to be a piece of cake for you as there are some properties of tuple which are similar to list and some are not.
Dictionary: This is the last member of the collection family which is also a collection of items stored in a single variable but the arrangement of storing the items is in key-value pair format. This is the unique structure amongst all the data types in Python. It also helps us to simulate real-life data arrangement.
Functions: It is one of the most important concepts of python. It helps us to avoid repeating a task again and again and distributing our program in such a way that our program gets categorized, clean, and easy to understand. It also helps us to make custom formulas and commands in Python.
File Handling: When we are talking about a huge amount of data, the problem of memory is very sure to come. The same goes with Python, the console screen is not big enough to store huge amounts of data that’s why we seek help with file-handling concepts in python. Basically, file handling helps us to store data in an external file and secure that data for a longer period.
If you wish to learn more about data science or want to curve your career in the data science field feel free to join our free workshop on Masters in Data Science with PowerBI, where you will get to know how exactly the data science field works and why companies are ready to pay handsome salaries in this field.
In this workshop, you will get to know each tool and technology from scratch that will make you skillfully eligible for any data science profile.
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Thinking, Why Console Flare?
- Recently, ConsoleFlare has been recognized as one of the Top 10 Most Promising Data Science Training Institutes of 2023.
- Console Flare offers the opportunity to learn Data Science in Hindi, just like how you speak daily.
- Console Flare believes in the idea of “What to learn and what not to learn” and this can be seen in their curriculum structure. They have designed their program based on what you need to learn for data science and nothing else.
- Want more reasons,
Register yourself on consoleflare and we will call you back.
