Python

Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training machine learning models on large datasets of existing content, which the model then uses to generate new and original content. 

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Popular Python libraries for Generative AI

Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available. Here are 10 of the top Python libraries for generative AI: 

 1. TensorFlow:

TensorFlow is a popular open-source machine learning library that can be used for a variety of tasks, including generative AI. TensorFlow provides a wide range of tools and resources for building and training generative models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

TensorFlow can be used to train and deploy a variety of generative models, including: 

• Generative adversarial networks (GANs) 
• Variational autoencoders (VAEs) 
• Transformer-based text generation models 
• Diffusion models 

TensorFlow is a good choice for generative AI because it is flexible and powerful, and it has a large community of users and contributors. 

2. PyTorch:

PyTorch is another popular open-source machine learning library that is well-suited for generative AI. PyTorch is known for its flexibility and ease of use, making it a good choice for beginners and experienced users alike. 

PyTorch can be used to train and deploy a variety of generative models, including: 

• Conditional GANs 
• Autoregressive models 
• Diffusion models 

PyTorch is a good choice for generative AI because it is easy to use and has a large community of users and contributors. 

3. Transformers:

Transformers is a Python library that provides a unified API for training and deploying transformer models. Transformers are a type of neural network architecture that is particularly well-suited for natural language processing tasks, such as text generation and translation.

Transformers can be used to train and deploy a variety of generative models, including: 

• Transformer-based text generation models, such as GPT-3 and LaMDA 

Transformers is a good choice for generative AI because it is easy to use and provides a unified API for training and deploying transformer models. 

4. Diffusers:

Diffusers is a Python library for diffusion models, which are a type of generative model that can be used to generate images, audio, and other types of data. Diffusers provides a variety of pre-trained diffusion models and tools for training and fine-tuning your own models.

Diffusers can be used to train and deploy a variety of generative models, including: 

• Diffusion models for image generation 
• Diffusion models for audio generation 

• Diffusion models for other types of data generation 

Diffusers is a good choice for generative AI because it is easy to use and provides a variety of pre-trained diffusion models. 

5. Jax:

Jax is a high-performance numerical computation library for Python with a focus on machine learning and deep learning research. It is developed by Google AI and has been used to achieve state-of-the-art results in a variety of machine learning tasks, including generative AI. Jax has a number of advantages for generative AI, including:

• Performance: Jax is highly optimized for performance, making it ideal for training large and complex generative models. 
• Flexibility: Jax is a general-purpose numerical computing library, which gives it a great deal of flexibility for implementing different types of generative models. 
• Ecosystem: Jax has a growing ecosystem of tools and libraries for machine learning and deep learning, which can be useful for developing and deploying generative AI applications. 

Here are some examples of how Jax can be used for generative AI: 

• Training generative adversarial networks (GANs) 
• Training diffusion models 
• Training transformer-based text generation models 
• Training other types of generative models, such as variational autoencoders (VAEs) and reinforcement learning-based generative models 

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6. LangChain: 

LangChain is a Python library for chaining multiple generative models together. This can be useful for creating more complex and sophisticated generative applications, such as text-to-image generation or image-to-text generation.

LangChain is a good choice for generative AI because it makes it easy to chain multiple generative models together to create more complex and sophisticated applications.  

7. LlamaIndex:

LlamaIndex is a Python library for ingesting and managing private data for machine learning models. LlamaIndex can be used to store and manage your training datasets and trained models in a secure and efficient way.

LlamaIndex is a good choice for generative AI because it makes it easy to store and manage your training datasets and trained models in a secure and efficient way. 

8. Weight and biases:

Weight and Biases (W&B) is a platform that helps machine learning teams track, monitor, and analyze their experiments. W&B provides a variety of tools and resources for tracking and monitoring your generative AI experiments, such as:

• Experiment tracking: W&B makes it easy to track your experiments and see how your models are performing over time. 
• Model monitoring: W&B monitors your models in production and alerts you to any problems. 
• Experiment analysis: W&B provides a variety of tools for analyzing your experiments and identifying areas for improvement. 

9. Acme:

Acme is a reinforcement learning library for TensorFlow. Acme can be used to train and deploy reinforcement learning-based generative models, such as GANs and policy gradients.

Acme provides a variety of tools and resources for training and deploying reinforcement learning-based generative models, such as: 

• Reinforcement learning algorithms: Acme provides a variety of reinforcement learning algorithms, such as Q-learning, policy gradients, and actor-critic. 
• Environments: Acme provides a variety of environments for training and deploying reinforcement learning-based generative models. 
• Model deployment: Acme provides tools for deploying reinforcement learning-based generative models to production. 

 Python libraries help in building generative AI applications

These libraries can be used to build a wide variety of generative AI applications, such as:

• Chatbots: Chatbots can be used to provide customer support, answer questions, and engage in conversations with users.
• Content generation: Generative AI can be used to generate different types of content, such as blog posts, articles, and even books.
• Code generation: Generative AI can be used to generate code, such as Python, Java, and C++.
• Image generation: Generative AI can be used to generate images, such as realistic photos and creative artwork.

Generative AI is a rapidly evolving field, and new Python libraries are being developed all the time. The libraries listed above are just a few of the most popular and well-established options.

The job market for data scientists is booming. In fact, the demand for data experts is expected to grow by 36% between 2021 and 2031, significantly higher than the average for all occupations. This is great news for anyone who is interested in a career in data science.

According to the U.S. Bureau of Labor Statistics, the job outlook for data science is estimated to be 36% between 2021–31, significantly higher than the average for all occupations, which is 5%. This makes it an opportune time to pursue a career in data science.

In this blog, we will explore the 10 best data science bootcamps you can choose from as you kickstart your journey in data analytics.

What are Data Science Bootcamps? 

Data science boot camps are intensive, short-term programs that teach students the skills they need to become data scientists. These programs typically cover topics such as data wrangling, statistical inference, machine learning, and Python programming. 

• Short-term: Bootcamps typically last for 3-6 months, which is much shorter than traditional college degrees. 
• Flexible: Bootcamps can be completed online or in person, and they often offer part-time and full-time options. 
• Practical experience: Bootcamps typically include a capstone project, which gives students the opportunity to apply the skills they have learned. 
• Industry-focused: Bootcamps are taught by industry experts, and they often have partnerships with companies that are hiring data scientists. 

10 Best Data Science Bootcamps

Without further ado, here is our selection of the most reputable data science boot camps.  

1. Data Science Dojo Data Science Bootcamp

• Delivery Format: Online and In-person
• Tuition: $2,659 to $4,500
• Duration: 16 weeks

Data Science Dojo Bootcamp is an excellent choice for aspiring data scientists. With 1:1 mentorship and live instructor-led sessions, it offers a supportive learning environment. The program is beginner-friendly, requiring no prior experience.

Easy installments with 0% interest options make it the top affordable choice. Rated as an impressive 4.96, Data Science Dojo Bootcamp stands out among its peers. Students learn key data science topics, work on real-world projects, and connect with potential employers.

Moreover, it prioritizes a business-first approach that combines theoretical knowledge with practical, hands-on projects. With a team of instructors who possess extensive industry experience, students have the opportunity to receive personalized support during dedicated office hours.

2. Springboard Data Science Bootcamp

• Delivery Format: Online
• Tuition: $14,950
• Duration: 12 months long

Springboard’s Data Science Bootcamp is a great option for students who want to learn data science skills and land a job in the field. The program is offered online, so students can learn at their own pace and from anywhere in the world.

The tuition is high, but Springboard offers a job guarantee, which means that if you don’t land a job in data science within six months of completing the program, you’ll get your money back.

3. Flatiron School Data Science Bootcamp

• Delivery Format: Online or On-campus (currently online only)
• Tuition: $15,950 (full-time) or $19,950 (flexible)
• Duration: 15 weeks long

Next on the list, we have Flatiron School’s Data Science Bootcamp. The program is 15 weeks long for the full-time program and can take anywhere from 20 to 60 weeks to complete for the flexible program. Students have access to a variety of resources, including online forums, a community, and one-on-one mentorship.

4. Coding Dojo Data Science Bootcamp Online Part-Time

• Delivery Format: Online
• Tuition: $11,745 to $13,745
• Duration: 16 to 20 weeks

Coding Dojo’s online bootcamp is open to students with any background and does not require a four-year degree or Python programming experience. Students can choose to focus on either data science and machine learning in Python or data science and visualization.

It offers flexible learning options, real-world projects, and a strong alumni network. However, it does not guarantee a job, requires some prior knowledge, and is time-consuming.

5. CodingNomads Data Science and Machine Learning Course

• Delivery Format: Online
• Tuition: Membership: $9/month, Premium Membership: $29/month, Mentorship: $899/month
• Duration: Self-paced

CodingNomads offers a data science and machine learning course that is affordable, flexible, and comprehensive. The course is available in three different formats: membership, premium membership, and mentorship. The membership format is self-paced and allows students to work through the modules at their own pace.

The premium membership format includes access to live Q&A sessions. The mentorship format includes one-on-one instruction from an experienced data scientist. CodingNomads also offers scholarships to local residents and military students.

6. Udacity School of Data Science

• Delivery Format: Online
• Tuition: $399/month
• Duration: Depends on the program

Udacity offers multiple data science bootcamps, including data science for business leaders, data project managers, and more. It offers frequent start dates throughout the year for its data science programs. These programs are self-paced and involve real-world projects and technical mentor support.

Students can also receive LinkedIn profiles and GitHub portfolio reviews from Udacity’s career services. However, it is important to note that there is no job guarantee, so students should be prepared to put in the work to find a job after completing the program.

7. LearningFuze Data Science Bootcamp

• Delivery Format: Online and in-person
• Tuition: $5,995 per module
• Duration: Multiple formats

LearningFuze offers a data science boot camp through a strategic partnership with Concordia University Irvine.

Offering students the choice of live online or in-person instruction, the program gives students ample opportunities to interact one-on-one with their instructors. LearningFuze also offers partial tuition refunds to students who are unable to find a job within six months of graduation.

The program’s curriculum includes modules in machine learning and deep learning and artificial intelligence. However, it is essential to note that there are no scholarships available, and the program does not accept the GI Bill.

8. Thinkful Data Science Bootcamp

• Delivery Format: Online
• Tuition: $16,950
• Duration: 6 months

Thinkful offers a data science boot camp which is best known for its mentorship program. It caters to both part-time and full-time students. Part-time offers flexibility with 20-30 hours per week, taking 6 months to finish. Full-time is accelerated at 50 hours per week, completing in 5 months.

Payment plans, tuition refunds, and scholarships are available for all students. The program has no prerequisites, so both fresh graduates and experienced professionals can take this program.

9. Brain Station Data Science Course Online

• Delivery Format: Online
• Tuition: $9,500 (part time); $16,000 (full time)
• Duration: 10 weeks

BrainStation offers an immersive and hands-on data science boot camp that is both comprehensive and affordable. Industry experts teach the program and includes real-world projects and assignments. BrainStation has a strong job placement rate, with over 90% of graduates finding jobs within six months of completing the program.

However, the program is expensive and can be demanding. Students should carefully consider their financial situation and time commitment before enrolling in the program.

10. BloomTech Data Science Bootcamp

• Delivery Format: Online
• Tuition: $19,950
• Duration: 6 months

BloomTech offers a data science bootcamp that covers a wide range of topics, including statistics, predictive modeling, data engineering, machine learning, and Python programming. BloomTech also offers a 4-week fellowship at a real company, which gives students the opportunity to gain work experience.

BloomTech has a strong job placement rate, with over 90% of graduates finding jobs within six months of completing the program. The program is expensive and requires a significant time commitment, but it is also very rewarding.

Here’s a guide to choosing the best data science bootcamp

What to expect in the best data science bootcamps?

A data science bootcamp is a short-term, intensive program that teaches you the fundamentals of data science. While the curriculum may be comprehensive, it cannot cover the entire field of data science.

Therefore, it is important to have realistic expectations about what you can learn in a bootcamp. Here are some of the things you can expect to learn in a data science bootcamp:

• Data science concepts: This includes topics such as statistics, machine learning, and data visualization.
• Hands-on projects: You will have the opportunity to work on real-world data science projects. This will give you the chance to apply what you have learned in the classroom.
• A portfolio: You will build a portfolio of your work, which you can use to demonstrate your skills to potential employers.
• Mentorship: You will have access to mentors who can help you with your studies and career development.
• Career services: Bootcamps typically offer career services, such as resume writing assistance and interview preparation.

Wrapping up

All and all, data science bootcamps can be a great way to learn the fundamentals of data science and gain the skills you need to launch a career in this field. If you are considering a boot camp, be sure to do your research and choose a program that is right for you.

Postman is a popular collaboration platform for API development used by developers all over the world. It is a powerful tool that simplifies the process of testing, documenting, and sharing APIs.

Postman provides a user-friendly interface that enables developers to interact with RESTful APIs and streamline their API development workflow. In this blog post, we will discuss the different HTTP methods, and how they can be used with Postman.

HTTP Methods

HTTP methods are used to specify the type of action that needs to be performed on a resource. There are several HTTP methods available, including GET, POST, PUT, DELETE, and PATCH. Each method has a specific purpose and is used in different scenarios:

• GET is used to retrieve data from an API.
• POST is used to create new data in an API.
• PUT is used to update existing data in an API.
• DELETE is used to delete data from an API.
• PATCH is used to partially update existing data in an API.

1. GET Method

The GET method is used to retrieve information from the server. It is the most used HTTP method and is used to retrieve data from a server.   

In Postman, you can use the GET method to retrieve data from an API endpoint. To use the GET method, you need to specify the URL in the request bar and click on the Send button. Here are step-by-step instructions for making requests using GET: 

 In this tutorial, we are using the following URL:

Step 1:  

Create a new request by clicking + in the workbench to open a new tab.  

Step 2: 

Enter the URL of the API that we want to test. 

Step 3: 

Select the “GET” method. 

Click the “Send” button. 

2. POST Method

The POST method is used to send data to the server. It is commonly used to create new resources on the server. In Postman, you can use the POST method to send data to the server. To use the POST method, you need to specify the URL in the request. Here are step-by-step instructions for making requests using POST

1. Create a new request.
2. Enter the URL of the API that you want to test.
3. Select the “POST” method.
4. Add any additional headers or parameters to the request.
5. Click the “Send” button.

3. PUT Method

PUT is used to update existing data in an API. In Postman, you can use the PUT method to update existing data in an API by selecting the “PUT” method from the drop-down menu next to the “Method” field.

You can also add data to the request body by clicking the “Body” tab and selecting the “raw” radio button. Here are step-by-step instructions for making requests using PUT

1. Create a new request.
2. Enter the URL of the API that you want to test.
3. Select the “PUT” method.
4. Add any additional headers or parameters to the request.
5. Click the “Send” button.

4. DELETE Method

DELETE is used to delete existing data in an API. In Postman, you can use the DELETE method to delete existing data in an API by selecting the “DELETE” method from the drop-down menu next to the “Method” field. Here are step-by-step instructions for making requests using DELETE

1. Create a new request.
2. Enter the URL of the API that you want to test.
3. Select the “DELETE” method.
4. Add any additional headers or parameters to the request.
5. Click the “Send” button.

5. PATCH Method

PATCH is used to partially update existing data in an API. In Postman, you can use the PATCH method to partially update existing data in an API by selecting the “PATCH” method from the drop-down menu next to the “Method” field.

You can also add data to the request body by clicking the “Body” tab and selecting the “raw” radio button. Here are step-by-step instructions for making requests using PATCH:

1. Create a new request.
2. Enter the URL of the API that you want to test.
3. Select the “PATCH” method.
4. Add any additional headers or parameters to the request.
5. Click the “Send” button.

Why Postman and Python are useful together

With the Postman Python library, developers can create and send requests, manage collections and environments, and run tests. The library also provides a command-line interface (CLI) for interacting with Postman APIs from the terminal. 

How does Postman work with REST APIs? 

• Creating Requests: Developers can use Postman to create HTTP requests for REST APIs. They can specify the request method, API endpoint, headers, and data. 
• Sending Requests: Once the request is created, developers can send it to the API server. Postman provides tools for sending requests, such as the “Send” button, keyboard shortcuts, and history tracking. 
• Testing Responses: Postman receives responses from the API server and displays them in the tool’s interface. Developers can test the response status, headers, and body. 
• Debugging: Postman provides tools for debugging REST APIs, such as console logs and response time tracking. Developers can easily identify and fix issues with their APIs. 
• Automation: Postman allows developers to automate testing, documentation, and other tasks related to REST APIs. Developers can write test scripts using JavaScript and run them using Postman’s test runner. 
• Collaboration: Postman allows developers to share API collections with team members, collaborate on API development, and manage API documentation. Developers can also use Postman’s version control system to manage changes to their APIs.

Wrapping up

In summary, Postman is a powerful tool for working with REST APIs. It provides a user-friendly interface for creating, testing, and documenting REST APIs, as well as tools for debugging and automation. Developers can use Postman to collaborate with team members and manage API collections or developers working with APIs. 

Written by Nimrah Sohail

If you’re interested in investing in the stock market, you know how important it is to have access to accurate and up-to-date market data. This data can help you make informed decisions about which stocks to buy or sell, when to do so, and at what price. However, retrieving and analyzing this data can be a complex and time-consuming process. That’s where Python comes in.

Python is a powerful programming language that offers a wide range of tools and libraries for retrieving, analyzing, and visualizing stock market data. In this blog, we’ll explore how to use Python to retrieve fundamental stock market data, such as earnings reports, financial statements, and other key metrics. We’ll also demonstrate how you can use this data to inform your investment strategies and make more informed decisions in the market.

So, whether you’re a seasoned investor or just starting out, read on to learn how Python can help you gain a competitive edge in the stock market.

How to retrieve fundamental stock market data using Python?

Python can be used to retrieve a company’s financial statements and earnings reports by accessing fundamental data of the stock.  Here are some methods to achieve this: 

1. Using the yfinance library:

One can easily get, read, and interpret financial data using Python by using the yfinance library along with the Pandas library. With this, a user can extract various financial data, including the company’s balance sheet, income statement, and cash flow statement. Additionally, yfinance can be used to collect historical stock data for a specific time period. 

2. Using Alpha Vantage:

Alpha Vantage offers a free API for enterprise-grade financial market data, including company financial statements and earnings reports. A user can extract financial data using Python by accessing the Alpha Vantage API. 

3. Using the get_quote_table method:

The get_quote_table method can be used to extract the data found on the summary page of a stock. This method extracts financial data from the summary page of stock and returns it in the form of a dictionary. From this dictionary, a user can extract the P/E ratio of a company, which is an important financial metric. Additionally, the get_stats_valuation method can be used to extract the P/E ratio of a company.

Python libraries for stock data retrieval: Fundamental and price data

Python has numerous libraries that enable us to access fundamental and price data for stocks. To retrieve fundamental data such as a company’s financial statements and earnings reports, we can use APIs or web scraping techniques.  

On the other hand, to get price data, we can utilize APIs or packages that provide direct access to financial databases. Here are some resources that can help you get started with retrieving both types of data using Python for data science: 

Retrieving fundamental data using API calls in Python is a straightforward process. An API or Application Programming Interface is a server that allows users to retrieve and send data to it using code.  

When requesting data from an API, we need to make a request, which is most commonly done using the GET method. The two most common HTTP request methods for API calls are GET and POST. 

After establishing a healthy connection with the API, the next step is to pull the data from the API. This can be done using the requests.get() method to pull the data from the mentioned API. Once we have the data, we can parse it into a JSON format. 

Top Python libraries like pandas and alpha_vantage can be used to retrieve fundamental data. For example, with alpha_vantage, the fundamental data of almost any stock can be easily retrieved using the Financial Data API. The formatting process can be coded and applied to the dataset to be used in future data science projects. 

Obtaining essential stock market information through APIs

There are various financial data APIs available that can be used to retrieve fundamental data of a stock. Some popular APIs are eodhistoricaldata.com, Nasdaq Data Link APIs, and Morningstar. 

• Eodhistoricaldata.com, also known as EOD HD, is a website that provides more than just fundamental data and is free to sign up for. It can be used to retrieve fundamental data of a stock.  
• Nasdaq Data Link APIs can be used to retrieve historical time-series of a stock’s price in CSV format. It offers a simple call to retrieve the data. 
• Morningstar can also be used to retrieve fundamental data of a stock. One can search for a stock on the website and click on the first result to access the stock’s page and retrieve its data. 
• Another source for fundamental financial company data is a free source created by a friend. All of the data is easily available from the website, and they offer API access to global stock data (quotes and fundamentals). The documentation for the API access can be found on their website. 

Once you have established a connection to an API, you can pull the fundamental data of a stock using requests. The fundamental data can then be parsed into JSON format using Python libraries such as pandas and alpha_vantage. 

Conclusion 

In summary, retrieving fundamental data using API calls in Python is a simple process that involves establishing a healthy connection with the API, pulling the data from the API using requests.get(), and parsing it into a JSON format. Python libraries like pandas and alpha_vantage can be used to retrieve fundamental data. 

Python is a powerful and versatile programming language that has become increasingly popular in the field of data science. One of the main reasons for its popularity is the vast array of libraries and packages available for data manipulation, analysis, and visualization.

10 Python packages for data science and machine learning

In this article, we will highlight some of the top Python packages for data science that aspiring and practicing data scientists should consider adding to their toolbox. 

1. NumPy 

NumPy is a fundamental package for scientific computing in Python. It supports large, multi-dimensional arrays and matrices of numerical data, as well as a large library of mathematical functions to operate on these arrays. The package is particularly useful for performing mathematical operations on large datasets and is widely used in machine learning, data analysis, and scientific computing. 

2. Pandas 

Pandas is a powerful data manipulation library for Python that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data easy and intuitive. The package is particularly well-suited for working with tabular data, such as spreadsheets or SQL tables, and provides powerful data cleaning, transformation, and wrangling capabilities. 

3. Matplotlib 

Matplotlib is a plotting library for Python that provides an extensive API for creating static, animated, and interactive visualizations. The library is highly customizable, and users can create a wide range of plots, including line plots, scatter plots, bar plots, histograms, and heat maps. Matplotlib is a great tool for data visualization and is widely used in data analysis, scientific computing, and machine learning. 

4. Seaborn 

Seaborn is a library for creating attractive and informative statistical graphics in Python. The library is built on top of Matplotlib and provides a high-level interface for creating complex visualizations, such as heat maps, violin plots, and scatter plots. Seaborn is particularly well-suited for visualizing complex datasets and is often used in data exploration and analysis. 

5. Scikit-learn 

Scikit-learn is a powerful library for machine learning in Python. It provides a wide range of tools for supervised and unsupervised learning, including linear regression, k-means clustering, and support vector machines. The library is built on top of NumPy and Pandas and is designed to be easy to use and highly extensible. Scikit-learn is a go-to tool for data scientists and machine learning practitioners. 

6. TensorFlow 

TensorFlow is an open-source software library for dataflow and differentiable programming across various tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. TensorFlow was developed by the Google Brain team and is used in many of Google’s products and services. 

7. SQLAlchemy

SQLAlchemy is a Python package that serves as both a SQL toolkit and an Object-Relational Mapping (ORM) library. It is designed to simplify the process of working with databases by providing a consistent and high-level interface. It offers a set of utilities and abstractions that make it easier to interact with relational databases using SQL queries. It provides a flexible and expressive syntax for constructing SQL statements, allowing you to perform various database operations such as querying, inserting, updating, and deleting data.

8. OpenCV

OpenCV (CV2) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage and is now maintained by Itseez. OpenCV is available for C++, Python, and Java. 

9. urllib 

urllib is a module in the Python standard library that provides a set of simple, high-level functions for working with URLs and web protocols. It includes functions for opening and closing network connections, sending and receiving data, and parsing URLs. 

10. BeautifulSoup 

BeautifulSoup is a Python library for parsing HTML and XML documents. It creates parse trees from the documents that can be used to extract data from HTML and XML files with a simple and intuitive API. BeautifulSoup is commonly used for web scraping and data extraction. 

Wrapping up 

In conclusion, these Python packages are some of the most popular and widely-used libraries in the Python data science ecosystem. They provide powerful and flexible tools for data manipulation, analysis, and visualization, and are essential for aspiring and practicing data scientists. With the help of these Python packages, data scientists can easily perform complex data analysis and machine learning tasks, and create beautiful and informative visualizations. 

If you want to learn more about data science and how to use these Python packages, we recommend checking out Data Science Dojo’s Python for Data Science course, which provides a comprehensive introduction to Python and its data science ecosystem. 

SQL (Structured Query Language) is an important tool for data scientists. It is a programming language used to manipulate data stored in relational databases. Mastering SQL concepts allows a data scientist to quickly analyze large amounts of data and make decisions based on their findings. Here are some essential SQL concepts that every data scientist should know:

First, understanding the syntax of SQL statements is essential in order to retrieve, modify or delete information from databases. For example, statements like SELECT and WHERE can be used to identify specific columns and rows within the database that need attention. A good knowledge of these commands can help a data scientist perform complex operations with ease.

Second, developing an understanding of database relationships such as one-to-one or many-to-many is also important for a data scientist working with SQL.

Here’s an interesting read about Top 10 SQL commands

Let’s dive into some of the key SQL concepts that are important to learn for a data scientist.  

1. Formatting Strings

We are all aware that cleaning up the raw data is necessary to improve productivity overall and produce high-quality decisions. In this case, string formatting is crucial and entails editing the strings to remove superfluous information.

For transforming and manipulating strings, SQL provides a large variety of string methods. When combining two or more strings, CONCAT is utilized. The user-defined values that are frequently required in data science can be substituted for the null values using COALESCE. Tiffany Payne  

2. Stored Methods

We can save several SQL statements in our database for later use thanks to stored procedures. When invoked, it allows for reusability and has the ability to accept argument values. It improves performance and makes modifications simpler to implement. For instance, we’re attempting to identify all A-graded students with majors in data science. Keep in mind that CREATE PROCEDURE must be invoked using EXEC in order to be executed, exactly like the function definition. Paul Somerville 

3. Joins

Based on the logical relationship between the tables, SQL joins are used to merge the rows from various tables. In an inner join, only the rows from both tables that satisfy the specified criteria are displayed. In terms of vocabulary, it can be described as an intersection. The list of pupils who have signed up for sports is returned. Sports ID and Student registration ID are identical, please take note. Left Join returns every record from the LEFT table, while Right Join only shows the matching entries from the RIGHT table. Hamza Usmani 

4. Subqueries

Knowing how to utilize subqueries is crucial for data scientists because they frequently work with several tables and can use the results of one query to further limit the data in the primary query. The nested or inner query is another name for it. The subquery is conducted before the main query and needs to be surrounded in parenthesis. It is referred to as a multi-line subquery and requires the use of multi-line operators if it returns more than one row. Tiffany Payne 

5. Left Joins vs Inner Joins

It’s easy to confuse left joins and inner joins, especially for those who are still getting their feet wet with SQL or haven’t touched the language in a while. Make sure that you have a complete understanding of how the various joins produce unique outputs. You will likely be asked to do some kind of join in a significant number of interview questions, and in certain instances, the difference between a correct response and an incorrect one will depend on which option you pick. Tom Miller 

6. Manipulation of dates and times

There will most likely be some kind of SQL query using date-time data, and you should prepare for it. For instance, one of your tasks can be to organize the data into groups according to the months or to change the format of a variable from DD-MM-YYYY to only the month. You should be familiar with the following functions:

– EXTRACT
– DATEDIFF
– DATE ADD, DATE SUB
– DATE TRUNC 

Olivia Tonks 

7. Procedural Data Storage 

Using stored procedures, we can compile a series of SQL commands into a single object in the database and call it whenever we need it. It allows for reusability and when invoked, can take in values for its parameters. It improves efficiency and makes it simple to implement new features.

Using this method, we can identify the students with the highest GPAs who have declared a particular major. One goal is to identify all A-students whose major is Data Science. It’s important to remember that, like a function declaration, calling a CREATE PROCEDURE with EXEC is necessary for the procedure to be executed. Nely Mihaylova 

8. Connecting SQL to Python or R 

A developer who is fluent in a statistical language, like Python or R, may quickly and easily use the packages of
language to construct machine learning models on a massive dataset stored in a relational database management system. A programmer’s employment prospects will improve dramatically if they are fluent in both these statistical languages and SQL. Data analysis, dataset preparation, interactive visualizations, and more may all be accomplished in SQL Server with the help of Python or R. Rene Delgado  

9. Features of windows

In order to apply aggregate and ranking functions over a specific window, window functions are used (set of rows). When defining a window with a function, the OVER clause is utilized. The OVER clause serves dual purposes:

– Separates rows into groups (PARTITION BY clause is used).
– Sorts the rows inside those partitions into a specified order (ORDER BY clause is used).
– Aggregate window functions refer to the application of aggregate
functions like SUM(), COUNT(), AVERAGE(), MAX(), and MIN() over a specific window (set of rows). Tom Hamilton Stubber  

10. The emergence of Quantum ML

With the use of quantum computing, more advanced artificial intelligence and machine learning models might be created. Despite the fact that true quantum computing is still a long way off, things are starting to shift as a result of the cloud-based quantum computing tools and simulations provided by Microsoft, Amazon, and IBM. Combining ML and quantum computing has the potential to greatly benefit enterprises by enabling them to take on problems that are currently insurmountable. Steve Pogson 

11. Predicates

Predicates occur from your WHERE, HAVING, and JOIN clauses. They limit the amount of data that has to be processed to run your query. If you say SELECT DISTINCT customer_name FROM customers WHERE signup_date = TODAY() that’s probably a much smaller query than if you run it without the WHERE clause because, without it, we’re selecting every customer that ever signed up!

Data science sometimes involves some big datasets. Without good predicates, your queries will take forever and cost a ton on the infra bill! Different data warehouses are designed differently, and data architects and engineers make different decisions about to lay out the data for the best performance. Knowing the basics of your data warehouse, and how the tables you’re using are laid out, will help you write good predicates that save your company a lot of money during the year, and just as importantly, make your queries run much faster.

For example, a query that runs quickly but simply touches a huge amount of data in Bigquery can be really expensive if you’re using on-demand pricing which scales with the amount of data touched by the query. The same query can be really cheap if you’re using Bigquery’s Flat-rate pricing or Snowflake, both of which are affected by how long your query takes to run, not how much data is fed into it. Kyle Kirwan 

12. Query Syntax

This is what makes SQL so powerful and much easier than coding individual statements for every task we want to complete when extracting data from a database. Every query starts with one or more clauses such as SELECT, FROM, or WHERE – each clause gives us different capabilities; SELECT allows us to define which columns we’d like returned in the results set; FROM indicates which table name(s) we should get our data from; WHERE allows us to specify conditions that rows must meet for them to be included in our result set etcetera! Understanding how all these clauses work together will help you write more effective and efficient queries quickly, allowing you to do better analysis faster! John Smith 

Here’s a list of Techniques for Data Scientists to Upskill with LLMs

Elevate your business with essential SQL concepts 

AI and machine learning, which have been rapidly emerging, are quickly becoming one of the top trends in technology. Developments in AI and machine learning are being seen all over the world, from big businesses to small startups.

Businesses utilizing these two technologies are able to create smarter systems for their customers and employees, allowing them to make better decisions faster.

These advancements in artificial intelligence and machine learning are helping companies reach new heights with their products or services by providing them with more data to help inform decision-making processes.

Additionally, AI and machine learning can be used to automate mundane tasks that take up valuable time. This could mean more efficient customer service or even automated marketing campaigns that drive sales growth through
real-time analysis of consumer behavior. Rajesh Namase

Are you interested in learning Python for Data Science? Look no further than Data Science Dojo’s Introduction to Python for Data Science course. This instructor-led live training course is designed for individuals who want to learn how to use the power of Python to perform data analysis, visualization, and manipulation. 

Python is a powerful programming language used in data science, machine learning, and artificial intelligence. It is a versatile language that is easy to learn and has a wide range of applications. In this course, you will learn the basics of Python programming and how to use it for data analysis and visualization.

Learn the basics of Python programming and how to use it for data analysis and visualization in Data Science Dojo’s Introduction to Python for Data Science course. This instructor-led live training course is designed for individuals who want to learn how to use Python to perform data analysis, visualization, and manipulation. 

Why learn Python for data science? 

Python is a popular language for data science because it is easy to learn and use. It has a large community of developers who contribute to open-source libraries that make data analysis and visualization more accessible. Python is also an interpreted language, which means that you can write and run code without the need for a compiler. 

Python has a wide range of applications in data science, including: 

• Data analysis: Python is used to analyze data from various sources such as databases, CSV files, and APIs. 
• Data visualization: Python has several libraries that can be used to create interactive and informative visualizations of data. 
• Machine learning: Python has several libraries for machine learning, such as scikit-learn and TensorFlow. 
• Web scraping: Python is used to extract data from websites and APIs.
  

Python for Data Science Course Outline 

Data Science Dojo’s Introduction to Python for Data Science course covers the following topics: 

• Introduction to Python: Learn the basics of Python programming, including data types, control structures, and functions. 
• NumPy: Learn how to use the NumPy library for numerical computing in Python. 
• Pandas: Learn how to use the Pandas library for data manipulation and analysis. 
• Data visualization: Learn how to use the Matplotlib and Seaborn libraries for data visualization. 
• Machine learning: Learn the basics of machine learning in Python using sci-kit-learn. 
• Web scraping: Learn how to extract data from websites using Python. 
• Project: Apply your knowledge to a real-world Python project.

Python is an important programming language in the data science field and learning it can have significant benefits for data scientists. Here are some key points and reasons to learn Python for data science, specifically from Data Science Dojo’s instructor-led live training program: 

• Python is easy to learn: Compared to other programming languages, Python has a simpler and more intuitive syntax, making it easier to learn and use for beginners. 
• Python is widely used: Python has become the preferred language for data science and is used extensively in the industry by companies such as Google, Facebook, and Amazon. 
• Large community: The Python community is large and active, making it easy to get help and support. 
• A comprehensive set of libraries: Python has a comprehensive set of libraries specifically designed for data science, such as NumPy, Pandas, Matplotlib, and Scikit-learn, making data analysis easier and more efficient. 
• Versatile: Python is a versatile language that can be used for a wide range of tasks, from data cleaning and analysis to machine learning and deep learning. 
• Job opportunities: As more and more companies adopt Python for data science, there is a growing demand for professionals with Python skills, leading to more job opportunities in the field. 

Data Science Dojo’s instructor-led live training program provides a structured and hands-on learning experience to master Python for data science. The program covers the fundamentals of Python programming, data cleaning and analysis, machine learning, and deep learning, equipping learners with the necessary skills to solve real-world data science problems.  

By enrolling in the program, learners can benefit from personalized instruction, hands-on practice, and collaboration with peers, making the learning process more effective and efficient.

Some common questions asked about the course 

• What are the prerequisites for the course? 

The course is designed for individuals with little to no programming experience. However, some familiarity with programming concepts such as variables, functions, and control structures is helpful. 

• What is the format of the course? 

The course is an instructor-led live training course. You will attend live online classes with a qualified instructor who will guide you through the course material and answer any questions you may have. 

• How long is the course? 

The course is four days long, with each day consisting of six hours of instruction. 

Explore the Power of Python for Data Science

If you’re interested in learning Python for Data Science, Data Science Dojo’s Introduction to Python for Data Science course is an excellent place to start. This course will provide you with a solid foundation in Python programming and teach you how to use Python for data analysis, visualization, and manipulation.  

With its instructor-led live training format, you’ll have the opportunity to learn from an experienced instructor and interact with other students.

Enroll today and start your journey to becoming a data scientist with Python.

This blog explores the difference between mutable and immutable objects in Python. 

Python is a powerful programming language with a wide range of applications in various industries. Understanding how to use mutable and immutable objects is essential for efficient and effective Python programming. In this guide, we will take a deep dive into mastering mutable and immutable objects in Python.

Mutable objects

In Python, an object is considered mutable if its value can be changed after it has been created. This means that any operation that modifies a mutable object will modify the original object itself. To put it simply, mutable objects are those that can be modified either in terms of state or contents after they have been created. The mutable objects that are present in python are lists, dictionaries and sets. 

Advantages of mutable objects 

• They can be modified in place, which can be more efficient than recreating an immutable object. 
• They can be used for more complex and dynamic data structures, like lists and dictionaries. 

Disadvantages of mutable objects 

• They can be modified by another thread, which can lead to race conditions and other concurrency issues. 
• They can’t be used as keys in a dictionary or elements in a set. 
• They can be more difficult to reason about and debug because their state can change unexpectedly.

Want to start your EDA journey? Well you can always get yourself registered at Python for Data Science.

While mutable objects are a powerful feature of Python, they can also be tricky to work with, especially when dealing with multiple references to the same object. By following best practices and being mindful of the potential pitfalls of using mutable objects, you can write more efficient and reliable Python code.

Immutable objects 

In Python, an object is considered immutable if its value cannot be changed after it has been created. This means that any operation that modifies an immutable object returns a new object with the modified value. In contrast to mutable objects, immutable objects are those whose state cannot be modified once they are created. Examples of immutable objects in Python include strings, tuples, and numbers.

Advantages of immutable objects 

• They are safer to use in a multi-threaded environment as they cannot be modified by another thread once created, thus reducing the risk of race conditions. 
• They can be used as keys in a dictionary because they are hashable and their hash value will not change. 
• They can be used as elements of a set because they are comparable, and their value will not change. 
• They are simpler to reason about and debug because their state cannot change unexpectedly. 

Disadvantages of immutable objects

• They need to be recreated if their value needs to be changed, which can be less efficient than modifying the state of a mutable object. 
• They take up more memory if they are used in large numbers, as new objects need to be created instead of modifying the state of existing objects. 

Python has become a popular programming language in the data science community due to its simplicity, flexibility, and wide range of libraries and tools. With its powerful data manipulation and analysis capabilities, Python has emerged as the language of choice for data scientists, machine learning engineers, and analysts.     

By learning Python, you can effectively clean and manipulate data, create visualizations, and build machine-learning models. It also has a strong community with a wealth of online resources and support, making it easier for beginners to learn and get started.   

This blog will navigate your path via a detailed roadmap along with a few useful resources that can help you get started with it.   

Step 1. Learn the basics of Python programming  

Before you start with data science, it’s essential to have a solid understanding of its programming concepts. Learn about basic syntax, data types, control structures, functions, and modules.  

Step 2. Familiarize yourself with essential data science libraries    

Once you have a good grasp of Python programming, start with essential data science libraries like NumPy, Pandas, and Matplotlib. These libraries will help you with data manipulation, data analysis, and visualization.   

This blog lists some of the top Python libraries for data science that can help you get started.  

Step 3. Learn statistics and mathematics  

To analyze and interpret data correctly, it’s crucial to have a fundamental understanding of statistics and mathematics.   This short video tutorial can help you to get started with probability.   

Additionally, we have listed some useful statistics and mathematics books that can guide your way, do check them out!  

Step 4. Dive into machine learning  

Start with the basics of machine learning and work your way up to advanced topics. Learn about supervised and unsupervised learning, classification, regression, clustering, and more.   

This detailed machine-learning roadmap can get you started with this step.   

Step 5. Work on projects  

Apply your knowledge by working on real-world data science projects. This will help you gain practical experience and also build your portfolio. Here are some Python project ideas you must try out!  

Step 6. Keep up with the latest trends and developments 

Data science is a rapidly evolving field, and it’s essential to stay up to date with the latest developments. Join data science communities, read blogs, attend conferences and workshops, and continue learning.  

Our weekly and monthly data science newsletters can help you stay updated with the top trends in the industry and useful data science & AI resources, you can subscribe here.   

Additional resources    

1. Learn how to read and index time series data using Pandas package and how to build, predict or forecast an ARIMA time series model using Python’s statsmodels package with this free course. 
2. Explore this list of top packages and learn how to use them with this short blog. 
3. Check out our YouTube channel for Python & data science tutorials and crash courses, it can surely navigate your way.
   

By following these steps, you’ll have a solid foundation in Python programming and data science concepts, making it easier for you to pursue a career in data science or related fields.   

For an in-depth introduction do check out our Python for Data Science training, it can help you learn the programming language for data analysis, analytics, machine learning, and data engineering. 

Wrapping up

In conclusion, Python has become the go-to programming language in the data science community due to its simplicity, flexibility, and extensive range of libraries and tools.

Data science model deployment can sound intimidating if you have never had a chance to try it in a safe space. Do you want to make a rest API or a full frontend app? What does it take to do either of these? It’s not as hard as you might think. 

In this series, we’ll go through how you can take machine learning models and deploy them to a web app or a rest API (using saturn cloud) so that others can interact. In this app, we’ll let the user make some feature selections and then the model will predict an outcome for them. But using this same idea, you could easily do other things, such as letting the user retrain the model, upload things like images, or conduct other interactions with your model. 

Just to be interesting, we’re going to do this same project with two frameworks, voila and flask, so you can see how they both work and decide what’s right for your needs. In a flask, we’ll create a rest API and a web app version.
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Our toolkit 

• saturn cloud (so you can deploy easily!) 
• flask 
• voila 
• plotly (python and js) 
• scikit-learn (for our model)
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Other helpful links 

• ipywidgets (helpful for voila) 
• codebook for the dataset 
• plotly.js cheat sheet 
• jinja (helpful for flask)
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The project – Deploying machine learning models

The first steps of our process are exactly the same, whether we are going for voila or flask. We need to get some data and build a model! I will take the us department of education’s college scorecard data, and build a quick linear regression model that accepts a few inputs and predicts a student’s likely earnings 2 years after graduation. (you can get this data yourself at https://collegescorecard.ed.gov/data/) 

About measurements 

According to the data codebook: “the cohort of evaluated graduates for earnings metrics consists of those individuals who received federal financial aid, but excludes those who were subsequently enrolled in school during the measurement year, died before the end of the measurement year, received a higher-level credential than the credential level of the field of the study measured, or did not work during the measurement year.” 

Load data 

I already did some data cleaning and uploaded the features I wanted to a public bucket on s3, for easy access. This way, I can load it quickly when the app is run. 

Format for training 

Once we have the dataset, this is going to give us a handful of features and our outcome. We just need to split it between features and target with scikit-learn to be ready to model. (note that all of these functions will be run exactly as written in each of our apps.) 

 Our features are: 

• Region: geographic location of college 
• Locale: type of city or town the college is in 
• Control: type of college (public/private/for-profit) 
• Cipdesc_new: major field of study (cip code) 
• Creddesc: credential (bachelor, master, etc) 
• Adm_rate_all: admission rate 
• Sat_avg_all: average sat score for admitted students (proxy for college prestige) 
• Tuition: cost to attend the institution for one year 

Our target outcome is earn_mdn_hi_2yr: median earnings measured two years after completion of degree. 

Train model 

We are going to use scikit-learn’s pipeline to make our feature engineering as easy and quick as possible. We’re going to return a trained model as well as the r-squared value for the test sample, so we have a quick and straightforward measure of the model’s performance on the test set that we can return along with the model object. 

Now we have a model, and we’re ready to put together the app! All these functions will be run when the app runs, because it’s so fast that it doesn’t make sense to save out a model object to be loaded. If your model doesn’t train this fast, save your model object and return it in your app when you need to predict. 

If you’re interested in learning some valuable tips for machine learning projects, read our blog on machine learning project tips.

Visualization 

In addition to building a model and creating predictions, we want our app to show a visual of the prediction against a relevant distribution. The same plot function can be used for both apps, because we are using plotly for the job. 

The function below accepts the type of degree and the major, to generate the distributions, as well as the prediction that the model has given. That way, the viewer can see how their prediction compares to others. Later, we’ll see how the different app frameworks use the plotly object. 

 

 This is the general visual we’ll be generating — but because it’s plotly, it’ll be interactive! 

You might be wondering whether your favorite visualization library could work here — the answer is, maybe! Every python viz library has idiosyncrasies and is not likely to be supported exactly the same for voila and flask. I chose plotly because it has interactivity and is fully functional in both frameworks, but you are welcome to try your own visualization tool and see how it goes.  

Wrapping up

Written by Stephanie Kirmer