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People from different roles want to keep their jobs, so they will work on developing their skills to fit the current market. It’s a competitive market and we’re seeing more and more people taking an interest in data science; there are thousands of online courses, bootcamps and Masters (MSc) in the industry.

Having said that, if you want to enter the world of data science, you need to know Python.

Python’s role in data science

Python was developed in March 1991 by Dutch programmer Guido van Rossum. The design places a strong emphasis on code legibility. The language and object-oriented approach are built to help new and current programmers write clear and understandable code, from small to large projects, and working with small to big data.

31 years later, Python is considered one of the best programming languages today.

Python includes various libraries and frameworks so you don't have to do everything from scratch. These pre-built components contain useful and readable code that you can implement in your program. For example, NumPy, Matplotlib, SciPy, BeautifulSoup, etc.

If you want to learn more about Python libraries, read the following article: Python Library Data Scientists Should Know in 2022.

Efficient, fast, and reliable, Python allows developers to create applications, perform analysis, and generate visual output with minimal effort. Everything you need to become a data scientist!

Setting up Python

If you want to become a data scientist, we'll help you get started with Python with a step-by-step guide:

Install Python

First, you need to download the latest version of Python. You can find the latest version by heading to the official website here.

Depending on your operating system, follow the installation instructions until the end.

Choose IDE or code editor

An IDE is an integrated development environment, a software application used by programmers to develop software code more efficiently. Code Editor serves the same purpose but is a text editor program.

If you're not sure which one to choose, I've provided a list of popular options:

Visual Studio Code （VSCode）
PyCharm
Jupyter Notebook

When I started my data science career, I used VSC and Jupyter Notebook and I found them very useful in my data science learning and interactive coding. Once you've selected a product that suits your needs, install it and complete the walkthrough on how to use them.

Learn the basics

Before you dive into comprehensive projects, you need to learn the basics. So let’s delve into them.

Variables and data types

Variable is the term used for a container that stores data values. Data values are of various data types such as integers, floats, strings, lists, tuples, dictionaries, etc. Learning these is very important to build your basic knowledge.

In the example below, the variable is a name and contains the value "John". The data type is string:.name = "John"

Operators and expressions

Operators are symbols that allow computational tasks such as addition, subtraction, multiplication, division, exponentiation, etc. An expression in Python is a combination of operators and operands.

For examplex = x + 1 0x = x + 10 x = x+ 10

control structure

Control structures make programming easier by specifying the flow of execution in your code. In Python, you need to learn several types of control structures, such as conditionals, loops, and exception handling.

For example:

if x > 0: 
    print("Positive") 
else: 
    print("Non-positive")

Function

A function is a block of code that runs only when called. You can create functions using keywords.def

For example

def greet(name): 
    return f"Hello, {name}!"

Modules and libraries

A module in Python is a file containing Python definitions and statements. It can define functions, classes and variables. A library is a collection of related modules or packages. You can use modules and libraries by importing them using statements.import

For example, I mentioned above that Python contains various libraries and frameworks such as NumPy. You can import these different libraries by running the following commands:

import numpy as np
import pandas as pd
import math
import random

You can use Python to import various libraries and modules.

use data

Once you have a better understanding of the basics and how they work, the next step is to use these skills to work with data. You will need to learn how to:

Import and export data using Pandas

Pandas is a widely used Python library in the field of data science as it provides a flexible and intuitive way to handle data sets of various sizes. Assuming you have a CSV file data, you can use pandas to import the dataset via:

import pandas as pd

example_data = pd.read_csv("data/example_dataset1.csv")

Data cleaning and manipulation

Data cleaning and manipulation are important steps in the data preprocessing phase of a data science project, as you take the raw data and comb through all its inconsistencies, errors, and missing values to transform it into a structured format that can be used for analysis.

Elements of data cleaning include:

Handling missing values
Duplicate data
abnormal
data conversion
Data type cleaning

Elements of data manipulation include:

Select and filter data
Sort data
Group data
Join and merge data
Create new variable
Rotation and crosstabulation

You will need to learn all these elements and how to use them in Python. To get started right away, you can learn data cleaning and preprocessing for data science with this free e-book.

Statistical Analysis

As a data scientist, you need to know how to comb through data to identify trends, patterns, and insights. You can do this through statistical analysis. This is the process of collecting and analyzing data to identify patterns and trends.

This stage is used to eliminate bias through numerical analysis, allowing you to further research, develop statistical models, etc. These conclusions are used in the decision-making process to make future predictions based on past trends.

There are 6 types of statistical analysis:

descriptive analysis
inferential analysis
predictive analysis
normative analysis
exploratory data analysis
cause and effect analysis

In this blog, I’ll dive deeper into exploratory data analysis.

Exploratory Data Analysis (EDA)

Once the data is cleaned and manipulated, it’s time to move on to the next step: exploratory data analysis. This is where a data scientist analyzes and investigates a dataset and creates a summary of the main features/variables to help them gain further insights and create data visualizations.

EDA tools include

Predictive modeling, such as linear regression
Clustering techniques such as K-means clustering
Dimensionality reduction techniques such as principal component analysis (PCA)
Single, bivariate, and multivariate visualizations

This stage of data science is probably the most difficult aspect and requires a lot of practice. Libraries and modules can help you, but you need to understand the task at hand and what your desired results are to determine what EDA tools you need.

data visualization

EDA is used to gain further insights and create data visualizations. As a data scientist, you create visualizations of your findings. This can be basic visualizations such as line charts, bar charts, and scatter plots, but you can get very creative, such as heat maps, zone charts, and bubble charts.

There are various data visualization libraries you can use, but these are the most popular:

Matplotlib
Seaborn
Plotly

Data visualization allows for better communication, especially with less technically inclined stakeholders.

Summarize

This blog aims to guide beginners on the steps they need to take to learn Python in their data science career. Each stage takes time and effort to master.

Original link: Introduction to Python Data Science (mvrlink.com)

Getting Started with Python Data Science