Five Steps to Learning Python for Data Science - Beginner’s Guide

 

Why should data scientists learn Python?

The preferred programming language for data scientists is Python. Although it wasn't the first primary programming language, its use has increased with time.

On Kaggle, the most popular website for data science competitions surpassed R in 2016.

Python was the most popular language among analytics professionals in 2018, with 66 percent of data scientists reporting using it daily.

What does the current data scientist job market look like?

A data scientist will make an average income of $119,118 in 2022, according to Glassdoor.

Both Python and data science look to have a promising future. As demand for data scientists rises, that number is only anticipated to climb. Fortunately, it's now simpler than ever to learn Python. We'll walk you through it in five easy stages.

How to learn Python for data science

Step -1 Learn Python fundamentals.

Everyone has a beginning. Learning the fundamentals of Python programming is the first step. If you're not already familiar with data science, you'll also want to get acquainted with it.

The fundamentals of Python can be learned in any order. The secret is to pick a direction and stick with it. This can be accomplished through online Bootcamps, data science course, self-study, or academic courses.

Step-2 Practice with hands-on learning

Hands-on learning is one of the finest methods to advance your knowledge.

Work on Python projects for Practice

You might be surprised by how quickly you pick things up when you create simple Python programs. Thankfully, almost every Dataquest course includes a project to help you learn more. Some of them are as follows:

Enjoy some fun while using Python and Jupyter Notebook to analyze a dataset of helicopter jail escapes.

Profitable App Profiles for Google Play and the App Store — In this supervised project, you'll perform data analysis work for a firm that creates mobile apps. Python will be used to add value through useful data analysis.

Examining posts on Hacker News Utilize a dataset of articles posted to the well-known technology website Hacker News.

Analyzing eBay's Car Sales Information Utilize Python to deal with a scraped dataset of used vehicles from eBay Kleinanzeigen, the German eBay website's classifieds area.

There is a tonne of additional beginner Python project ideas on this page as well:

• Create a game of rock, paper, and scissors.

• Make a text-based adventure game.

• Construct a guessing game.

• Create engaging Mad Libs

Alternative methods for learning and practicing

Read manuals, blogs, Python tutorials, or other people's open-source code for fresh ideas to improve your coursework and find solutions to the Python programming issues you run across.

Step-3 Learn python data science libraries.

The four most important Python libraries are Scikit-learn, Pandas, Matplotlib, and NumPy.

Several mathematical and statistical processes are made simpler by the NumPy library, which also serves as the foundation for many pandas library features.

Pandas is a Python package designed to make dealing with data easier. The mainstay of a lot of Python data science work is this.

A visualization package called Matplotlib makes it quick and simple to create charts from your data. 

Step-4 Build a data science portfolio as you go.

A portfolio is a must for aspirant data scientists because it's one of the key qualities hiring managers to look for in a prospect.

These projects should include working with various datasets, and each one should present intriguing insights you found. Consider the following project categories:

• Data cleaning project- Since the majority of data in the actual world needs to be cleaned, every project you clean up and evaluate will impress future employers.

• Data visualization project-The ability to create appealing, simple-to-read visualizations is a programming and design challenge, but if you succeed, your analysis will be much more beneficial. Your portfolio will stand out if a project includes attractive charts.

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• Machine Learning Project – If you want to become a data scientist, you must have a project demonstrating your ML proficiency. Several machine learning initiatives, each centered on a different algorithm, can be what you need.

Step-5 Apply advanced data science techniques.

Finally, develop your abilities. Gain confidence with the k-means clustering, classification, and regression models. You may also get started with machine learning by learning about bootstrapping models and building neural networks with Scikit-learn. Although learning new things will be continuous in your data science path, there are advanced Python courses you can take to be sure you've covered everything.

To advance your data science skills, you can check out Learnbay, which offers industry-oriented data science courses in Mumbai. Gain the skills that are now in high demand and become a data scientist or analyst with IBM certification.

Top 6 Core Data Science Concepts for Beginners

 When it comes to expanding one's career horizons, data science is quickly becoming a popular topic. Additionally, it has found applications in practically every industry. Although there is still much to learn and many developments in the field of data science, a core set of fundamental concepts is still crucial. So, before an interview or just to brush up on the basics, here are twenty of the most important concepts you should know.

 

1. Dataset 

 

As its name suggests, data science is a subfield of science that uses the scientific method to analyze data to investigate the connections between various properties and derive meaningful conclusions from these connections. Data is thus the central element of data science. 

A dataset is a specific instance of data currently used for analysis or model construction. A dataset can be composed of various types of information, including categorical and numerical data, as well as text, image, audio, and video data. A dataset may be static (constantly the same) or dynamic (changes with time, for example, stock prices). Additionally, a dataset could be space-dependent.

For instance, temperature data would range greatly between the United States and Africa. The most common dataset type for beginning data science projects is one that contains numerical data, which is often saved in a comma-separated values (CSV) file format.

2.  Data wrangling

            The process of transforming data from an unorganized state into one ready for analysis is known as "data wrangling." Data import, cleaning, structuring, string processing, HTML parsing, handling dates and times, handling missing data, and text mining are just a few of the procedures that make up the crucial stage of the data wrangling preprocessing process.

A crucial step for any data scientist is the practice of data wrangling. In a data science project, data is rarely readily available for analysis. The likelihood of the information being in a file, database, or extracted from a document like a web page, tweet, or PDF is higher. You will be able to extract important insights from your data that would otherwise be concealed if you know how to manage and clean data. 

You can find detailed information about data wrangling in a data science course. 

3. Data visualization

The most significant area of data science is data visualization. It is one of the primary methods used to examine and research the connections between various variables. Descriptive analytics can use data visualization (such as scatter plots, line graphs, bar plots, histograms, Q-Q plots, smooth densities, box plots, pair plots, heat maps, etc.). Additionally, machine learning employs data visualization for feature selection, model construction, model testing, and model evaluation. Remember that creating a data visualization is more of an art than a science when creating one. You need to combine multiple bits of code to create a high-quality visualization. 

4. Outliers 

A data point that deviates significantly from the rest of the dataset is known as an outlier. Outliers are frequently just inaccurate data, such as those caused by a broken sensor, tainted studies, or human error in data recording. However, outliers can occasionally point to an actual problem, like a flaw in the system. In huge datasets, outliers are predicted and are highly common. A box plot is a popular tool for finding outliers in a dataset.

Outliers can dramatically reduce a machine learning model's prediction power. Simply leaving out the data points is a standard approach to dealing with outliers. However, removing outliers in actual data can be overly optimistic and result in unreliable models. The RANSAC method is one of the more sophisticated approaches to handling outliers.

5. Data imputation

Missing values are common in datasets. The easiest technique to deal with missing data is to just throw away the data point. However, removing samples or dropping entire feature columns is impossible since we risk losing an excessive amount of important data. In this instance, we can approximate the missing values from the other training samples in our dataset using various interpolation approaches. Mean imputation, which replaces the missing value with the mean value of the whole feature column, is one of the most popular interpolation approaches.

6. Data scaling

Missing values are common in datasets. Throwing away the data point is the simplest solution for dealing with missing data. However, removing samples or dropping entire feature columns is impossible since we risk losing an excessive amount of important data. In this instance, we can approximate the missing values from the other training samples in our dataset using various interpolation approaches. Mean imputation, which replaces the missing value with the mean value of the whole feature column, is one of the most popular interpolation approaches.

We could choose to utilize either feature normalization or feature standardization to scale features to the same value. We tend to default to standardization and assume that data is regularly distributed, although this is not always the case. Therefore, it's critical to consider how your features are statistically distributed before selecting whether to apply normalization or standardization.

You might be familiar with these concepts but want to further your knowledge; you can look into the data science course in Mumbai, which Learnbay offers. Learners get 3 years of LM subscription and learn at their own pace. 

          

Top Benefits of Blockchain For Data Science

    

While data science focuses on using data for effective administration, blockchain's decentralized ledger provides data protection. These technologies have tons of unrealized potential that can boost productivity and efficiency.

• Enables Data Traceability

Peer-to-peer interactions are facilitated by blockchain. Any peer can review the complete procedure and determine how the results were acquired, for instance, if a published account improperly explains any approach.

Anyone may learn whether data is reliable to use, how to keep it, how to update it, where it comes from, and its appropriate utilization thanks to the ledger's transparent channels. In conclusion, blockchain technology will let consumers follow data from its point of entry to its exit point.

• Makes Real-Time Analysis Possible

Real-time data analysis is quite challenging. Real-time monitoring of changes is seen to be the most effective method of spotting scammers. However, real-time analysis was not feasible for a very long period. Due to the distributed nature of blockchain, businesses may now identify any irregularities in the database right away.

Spreadsheets have the feature of allowing users to view data changes in real-time. Similarly, blockchain also permits simultaneous work on the same type of information by two or more persons.

To know more about its techniques, check out the data science course, and get a chance to work on various projects. 

• Guarantees Data Quality 

Blockchain's digital ledger's data is kept in a variety of nodes, including both private and public ones. The information is cross-checked and analyzed at the entry point before adding it to further blocks. The technique itself can be used to check the data.

• Makes Data Sharing Easier

When data is transferred easily and smoothly, organizations can benefit greatly. With paper records, it is exceedingly challenging. This problem is made considerably more challenging when the information inside is needed somewhere else. Although it is true that these documents would eventually reach the other department, it might take a while and run the danger of getting lost in the mail.

Due to its ability to enable simultaneous and real-time access to data by two or more users, blockchain is currently the subject of most data scientists' fascination. Therefore, the administrative process is expedited when information flows freely.

• Ensures Trust

You must be conscious that prejudices frequently arise when there is only one authority. Too much reliance on a single person can be dangerous. Because of trust difficulties, many businesses refuse to give any third party access to their data. Sharing information becomes essentially impossible as a result of this. The lack of trust no longer prevents sharing information, thanks to blockchain technology. Sharing the knowledge at their disposal allows organizations to collaborate efficiently.

• Improves Data Integrity

 The improvement of data storage capacity was the primary emphasis of organizations during the past ten years. That was settled toward the end of 2017. The majority of organizations' current focus is on safeguarding and confirming the data's integrity.

The primary cause of this is that organizations collect data from many centers. Even data generated internally or taken from government offices may have inaccuracies. Additionally, other data sources, like social media, may potentially turn out to be unreliable.

Data scientists are now using blockchain technology to verify data authenticity and follow it throughout the entire chain. Its unchangeable security is one of the factors in its widespread use. Thanks to multiple signatures, data is safeguarded at every stage using the blockchain's decentralized ledger. Anyone wishing to access the data must be given precise signatures. As a result, there are fewer data breaches and hacking incidents.

Interested in pursuing a career in data science and AI?

Learnbay has got your back. It offers the best IBM-accredited data science course in Mumbai, along with hands-on practical training in multiple domains. 

5 Must-Have Programming Languages For Data Science Career

 

Data science is the fastest-growing field in the tech world. Data science is a combination of computer programming, statistics, and domain expertise. Particularly, programming languages act as an essential tool for data analysis. Because of their robust libraries, several of these languages might look familiar to you, but not all of them are created the same way! Each language is important for data science. Therefore ideally, you should know at least a little bit about each one.

Let's examine each language in more detail:

1. PYTHON 

  

The most often used programming language in data science in Python. Despite this, some people might believe that Python is only employed in the scientific research sector.

Python provides strong libraries for data analysis, machine learning, and data visualization. Python is often used for additional purposes in software engineering.

Python is frequently considered one of the simplest programming languages to learn for data science novices. It is a wonderful place to start if you're interested in learning a programming language for data science. If you want to build several Python data science projects, visit a data science course and level up your coding skills.

2. R

 In the scientific community, data scientists frequently employ the statistical coding language R. Despite being less well-known, Python nonetheless has a great collection of data tools that are kept in the form of packages. I think the ggplot 2 visualization program is my favorite!

You may clean, analyze, and graph your data using the statistical computing and graphics programming language R. Researchers from various fields commonly use it to estimate and present findings, as well as statisticians and research methodology instructors.

3. STRUCTURED QUERY LANGUAGE [SQL]

            

A standardized programming language called Structured Query Language (SQL) is used to administer relational databases and carry out various operations on the data they contain. Originally developed in the 1970s, SQL is frequently used by database administrators and programmers creating scripts for data integration and data analysts setting up and running analytical queries.

Built-in Query, the preferred language for relational database queries is language. Most data scientists and analysts choose the data they require for analysis using SQL.

4. JAVASCRIPT

 

The least used scripting language in data science is Javascript. But because it is widely used in software engineering and in data visualization, I decided to include it.

Data can be displayed in stunning charts using the D3.js package, which is a feature of Javascript. This is far more dynamic than the data visualization libraries offered in Python and R.

Programmers and developers all over the world use Javascript to make dynamic and interactive online apps and browsers. 97.0 percent of all websites utilize JavaScript as a client-side programming language, making it the most widely used programming language in the world.

5. GIT

A distributed version control system that is open source is called Git. There are a variety of words used to describe Git. Let me clarify the language and break it down: Control Method: Git is essentially a content tracker.

I hope you’ve got a clear understanding of the popular programming languages used by data scientists. If you want to learn to program, a data science course in Mumbai can help you excel at the programming languages required for your data science journey. Enroll with Learnbay today and become an IBM-certified data scientist and analyst.

                 

Top Data Science Tools and Technologies in 2022

 

A wide range of data science tools and technologies have been developed due to the rapid rise in the popularity of data science for the general gain and benefit of data science enthusiasts.

A new and widely used word around the globe is data. And most digital behemoths, like Google, Facebook, Microsoft, IBM, and a great number of other significant and minor businesses, are spending their precious time and resources heavily on data and the field of data science.

In this essay, we'll examine eleven amazing tools and technologies that you really must be familiar with. They can be used for building models, starting projects, evaluating data, planning deployments, and so much more! They will greatly assist in developing some original and interesting Python and Data Science projects.

The top 10 technologies and tools that every data scientist should investigate for greater exposure and increased productivity:

1. GitHub (And Git)

One of the basic qualifications for a Data Scientist is familiarity with GitHub. The best location to display code and talk about projects with a great community is GitHub. Your work can be shared in repositories or as code snippets known as Gists, which are accessible to a wide range of users that visit your profile.

Microsoft's subsidiary GitHub, Inc. offers to host Git version control and software development. It provides its own features in addition to Git's distributed version control and source code management (SCM) capabilities. Every project offers access control in addition to several collaborative tools like wikis, task management, continuous integration, issue tracking, and feature requests.

2.  IDEs 

The Integrated Development Environment (IDE) software offers complete tools for the compilation and interpretation of programs. With source code editors, automation tools, and a debugger, it offers a platform for programmers, hobbyists, and developers to experiment with and analyze code and applications.

Python is a well-liked modern language. An IDE can support a single programming language, such as Pycharm, which is only compatible with Python, or it can support a wide range of programming languages, like Visual Studio Code. Therefore, various programming tools, including Pycharm, Visual Studio Code, Jupyter notebooks, etc., are readily available.

Because you may utilize each code block separately and you have the choice to use markdowns, I would also strongly advise using the Jupyter Notebook. It is commonly employed in many successful businesses. Anyone can use these Notebooks to collaborate on code more effectively and efficiently. You may find a comprehensive overview of Jupyter Notebooks in the section below.

3. GPUs

An electronic circuit known as a graphics processing unit is specialized and built to quickly manipulate and change memory in order to speed up the production of images in a frame buffer that is meant to be output to a display device.

Modern computing relies heavily on GPUs. Computational science and AI are changing due to GPU computing and high-performance networking. The development of GPUs is a major component in the current progress of deep learning.

Since they can handle numerous computations at once, GPUs are ideal for developing deep learning and artificial intelligence models. They have many cores, making it possible for many concurrent processes to compute more effectively.

4.  IBM Watson Studio

   

IBM's software platform for data science is called Watson Studio, formerly known as Data Science Experience or DSX. The platform comprises a workspace with numerous open-source collaborative tools for data research.

A data scientist can work on a project in Watson Studio with a team of collaborators who all have access to different analytics models and speak different languages (R/Python/Scala). Along with other capabilities like a managed Spark service and data sharing features, Watson Studio integrates common open source tools like RStudio, Spark, and Python in a controlled and secure environment.

5.   Google Cloud Platform

The Google Cloud Platform (GCP) is a set of cloud computing services that Google offers. It utilizes the same internal infrastructure as Google does for its end-user products, including Google Search, Gmail, file storage, and YouTube.

Google Cloud Platform offers serverless computing environments, platform as a service, and infrastructure as a service. In addition to a set of management tools, it provides a variety of modular cloud services, such as computing, data storage, data analytics, and machine learning. An account number or credit card is needed for registration.

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