Quote for the day ! Mistakes are a fact of life.It is the response to the error that counts.
Select Page

Python and Data Science

by | Jul 18, 2021 | Uncategorized | 0 comments

Python for Data Science

Python is a general-purpose programming language  and  is becoming ever more popular than others for data science. It allows  you to work quickly and integrate with systems easily.

Data Science

Data science is one of the most promising and in-demand career paths for skilled professionals nowadays. Successful data professionals understand that Python is very helpful to achieve their goals in professional life as a Data scientist.

How to learn Python

 

Most aspiring data scientists begin to learn Python by taking programming courses meant for developers. They also start solving Python programming riddles on websites like LeetCode with an assumption that they have to get good at programming concepts before starting to analyzing data using Python.

This is a huge mistake because data scientists use Python for retrieving, cleaning, visualizing and building models; and not for developing software applications. Therefore, you have to focus most of your time in learning the modules and libraries in Python to perform these tasks.

6 Simple steps to learn Python

  1. Step 0: Figure out what you need to learn.
  2. Step 1: Get comfortable with Python.
  3. Step 2: Learn data analysis, manipulation, and visualization with pandas.
  4. Step 3: Learn machine learning with scikit-learn.
  5. Step 4: Understand machine learning in more depth.
  6. Step 5: Keep learning and practicing.

How Long Will It Take To Learn Python?

For data science specifically, estimates a range from three months to a year of consistent practice.

Really, it all depends on your desired timeline, free time that you can dedicate to learn Python programming and the pace at which you learn.

Where Can I Learn Python

Python is also used in a variety of other programming disciplines from game development to mobile apps. Generic “learn Python” resources try to teach a bit of everything, but this means you’ll be learning quite a few things that aren’t actually relevant to data science work.

Moreover, working on something that doesn’t feel connected to your goals can feel really demotivating. If you want to be doing data analysis and instead you’re struggling through a course that’s teaching you to build a game with Python, it’s going to be easy to get frustrated and quit.

How is Python Used for Data Science?

Programming languages like Python are used at every step in the data science process. For example, a data science project workflow might look something like this:

Using Python and SQL, you write a query to pull the data you need from your company database.

Using Python library, you clean and sort the data into a dataframes (table) that’s ready for analysis.

Using Python  libraries, you begin analyzing, exploring, and visualizing the data.

Using Python  library to build a predictive model that forecasts future outcomes for your company based on the data you pulled.

You arrange your final analysis and your model results into an suitable format for contacting with your colleagues.

Why Python is so popular to handle large volume of data

Python is slow for numerically large algorithms and handling large volumes of data. You might ask then why is Python the most popular programming language for data science ?

The answer is that in Python, it is easy to offload number-crunching tasks to the lower layer in the form of a C or Fortran extension. That is exactly what Numpy and Pandas do ( These are some of the standard libraries in Python)

First, you should learn Numpy. It is the most fundamental module for scientific computing with Python. Numpy provides the support of highly optimized multidimensional arrays, which are the most basic data structure of most Machine Learning algorithms.

Next, you should learn Pandas. Data scientists spend most of their time cleaning data, which is also called as data munging or data wrangling.

Pandas is the most popular Python library for manipulating data. Pandas is as an extension of NumPy. The underlying code for Pandas uses the NumPy library extensively. The primary data structure in Pandas is called a data frame.

How to use SQL and Python


In organizations, data resides in a database. Therefore, we need to know how to retrieve data using SQL and perform the analysis using Python.

Data Scientists manipulate data using both SQL and Pandas. Because there are certain data manipulation tasks that are easy to perform using SQL, and there are certain tasks that can be done efficiently using Pandas. I personally like to use SQL for retrieving data and do the manipulation in Pandas.

Today, companies use analytics platforms like Mode Analytics and Databricks to easily work with Python and SQL.

Basic Statistics with Python


Most aspiring Data Scientists directly jump to learn machine learning without even learning

the basics of statistics.

Don’t make that mistake because Statistics is the backbone of data science. On the other hand, aspiring data scientists who learn statistics just learn the theoretical concepts instead of learning the practical concepts.

Some of the basic Statistical concepts you should know before using Python

Sampling, frequency distributions, Mean, Median, Mode, Measure of variability,

Probability basics, significant testing, standard deviation, z-scores, confidence intervals

and hypothesis testing

Conclusion

Python is a general-purpose programming language  and  is becoming ever more popular recently.

Data science is one of the most promising and in-demand career paths for skilled professionals

Data scientists use Python for retrieving, cleaning, visualizing and building models

Python is also used in a variety of other programming disciplines from game development to mobile apps

Using Python and SQL, you write a query to pull the data you need from your company database.

Using Python library, you clean and sort the data into a dataframes (table) that’s ready for analysis.

Using Python  libraries, you begin analyzing, exploring, and visualizing the data.

Using Python  library to build a predictive model that forecasts future outcomes for your company based on the data you pulled.

First, you should learn Numpy. It is the most fundamental module for scientific computing with Python. Numpy provides the support of highly optimized multidimensional arrays

Next, you should learn Pandas. Data scientists spend most of their time cleaning data, which is also called as data munging or data wrangling.

Do you want to know Why Python is a special Language for Programming CLICK HERE

Do you want to work online and earn money CLICK HERE

Blog Technical Support Developing Resources