# data[].attributes.headline
Online Free Course: Data Science with Analogies, Algorithms and Solved Problems

# data[].attributes.subject
Online Free Course: Data Science with Analogies, Algorithms and Solved Problems

# data[].attributes.attachment_link.readable_text
To understand how these different domains are distinct and how they collaborate as well.

To really understand where these concepts are used using real life analogies.

To understand the different algorithms and their working.

To learn how these algorithms are applied to solve various problems.

# data[].attributes.attachment_link.title
Free Data Science Tutorial - Data Science with Analogies, Algorithms and Solved Problems

# data[].attributes.attachment_link.description
To understand how these different domains are distinct and how they collaborate as well. To really understand where these concepts are used using real life analogies. To understand the different algorithms and their working. To learn how these algorithms are applied to solve various problems.

# data[].attributes.headline
Online Free Course: Introduction to Data Science using Python (Module 1/3)

# data[].attributes.subject
Online Free Course: Introduction to Data Science using Python (Module 1/3)

# data[].attributes.attachment_link.readable_text
I am a coder, manager, educator and a gamer. I love data and analytics. In my day job, I work with database technologies including SQL , Big Data and Tableau. I am passionate about technologies and love coding and managing teams. In my spare time I like to teach Big Data analytics, Databases, Programming etc. I am currently working on certain machine learning and Data Science projects and love to explore more in the Statistics field.

# data[].attributes.attachment_link.title
Free Data Science Tutorial - Introduction to Data Science using Python (Module 1/3)

# data[].attributes.attachment_link.description
I am a coder, manager, educator and a gamer. I love data and analytics. In my day job, I work with database technologies including SQL , Big Data and Tableau. I am passionate about technologies and love coding and managing teams. In my spare time I like to teach Big Data analytics, Databases, Programming etc.

# data[].attributes.headline
Interactive Course: Data Types for Data Science in Python

# data[].attributes.subject
Interactive Course: Data Types for Data Science in Python

# data[].attributes.attachment_link.readable_text
1. This chapter will introduce you to the fundamental Python data types - lists, sets, and tuples. These data containers are critical as they provide the basis for storing and looping over ordered data. To make things interesting, you'll apply what you learn about these types to answer questions about the New York Baby Names dataset!

2. The collections module is part of Python's standard library and holds some more advanced data containers. You'll learn how to use the Counter, defaultdict, OrderedDict and namedtuple in the context of answering questions about the Chicago transit dataset.

3. Time for a case study to reinforce all of your learning so far! You'll use all the containers and data types you've learned about to answer several real world questions about a dataset containing information about crime in Chicago. Have fun!

4. At the root of all things Python is a dictionary. Herein, you'll learn how to use them to safely handle data that can viewed in a variety of ways to answer even more questions about the New York Baby Names dataset. You'll explore how to loop through data in a dictionary, access nested data, add new data, and come to appreciate all of the wonderful capabilities of Python dictionaries.

5. Handling times can seem daunting at time, but here, you'll dig in and learn how to create datetime objects, print them, look to the past and to the future. Additionally, you'll learn about some third party modules that can make all of this easier. You'll continue to use the Chicago Transit dataset to answer questions about transit times.

1. This chapter will introduce you to the fundamental Python data types - lists, sets, and tuples. These data containers are critical as they provide the basis for storing and looping over ordered data. To make things interesting, you'll apply what you learn about these types to answer questions about the New York Baby Names dataset!

2. At the root of all things Python is a dictionary. Herein, you'll learn how to use them to safely handle data that can viewed in a variety of ways to answer even more questions about the New York Baby Names dataset. You'll explore how to loop through data in a dictionary, access nested data, add new data, and come to appreciate all of the wonderful capabilities of Python dictionaries.

3. The collections module is part of Python's standard library and holds some more advanced data containers. You'll learn how to use the Counter, defaultdict, OrderedDict and namedtuple in the context of answering questions about the Chicago transit dataset.

4. Handling times can seem daunting at time, but here, you'll dig in and learn how to create datetime objects, print them, look to the past and to the future. Additionally, you'll learn about some third party modules that can make all of this easier. You'll continue to use the Chicago Transit dataset to answer questions about transit times.

5. Time for a case study to reinforce all of your learning so far! You'll use all the containers and data types you've learned about to answer several real world questions about a dataset containing information about crime in Chicago. Have fun!

"I've used other sites, but DataCamp's been the one that I've stuck with."

Devon Edwards Joseph

Lloyd's Banking Group

"DataCamp is the top resource I recommend for learning data science."

Louis Maiden

Harvard Business School

"DataCamp is by far my favorite website to learn from."

Ronald Bowers

Decision Science Analytics @ USAA

# data[].attributes.attachment_link.description
This chapter will introduce you to the fundamental Python data types - lists, sets, and tuples. These data containers are critical as they provide the basis for storing and looping over ordered data. To make things interesting, you'll apply what you learn about these types to answer questions about the New York Baby Names dataset!

# data[].attributes.headline
Interactive Course: Python Data Science Toolbox (Part 2)

# data[].attributes.subject
Interactive Course: Python Data Science Toolbox (Part 2)

# data[].attributes.attachment_link.readable_text
1. You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you'll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data-in this case, data from Twitter that you will load in chunks using iterators.

2. This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset-the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python data science chops.

In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists-and lists of lists-in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python data science toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

1. You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you'll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data-in this case, data from Twitter that you will load in chunks using iterators.

2. In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists-and lists of lists-in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python data science toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

3. This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset-the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python data science chops.

"I've used other sites, but DataCamp's been the one that I've stuck with."

Devon Edwards Joseph

Lloyd's Banking Group

"DataCamp is the top resource I recommend for learning data science."

Louis Maiden

Harvard Business School

"DataCamp is by far my favorite website to learn from."

Ronald Bowers

Decision Science Analytics @ USAA

# data[].attributes.attachment_link.description
You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you'll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data-in this case, data from Twitter that you will load in chunks using iterators.

# data[].attributes.headline
Interactive Course: Python Data Science Toolbox (Part 1)

# data[].attributes.subject
Interactive Course: Python Data Science Toolbox (Part 1)

# data[].attributes.attachment_link.readable_text
1. In this chapter, you'll learn how to write simple functions, as well as functions that accept multiple arguments and return multiple values. You'll also have the opportunity to apply these new skills to questions commonly encountered by data scientists.

2. Learn about lambda functions, which allow you to write functions quickly and on the fly. You'll also practice handling errors in your functions, which is an essential skill. Then, apply your new skills to answer data science questions.

In this chapter, you'll learn to write functions with default arguments so that the user doesn't always need to specify them, and variable-length arguments so they can pass an arbitrary number of arguments on to your functions. You'll also learn about the essential concept of scope.

1. In this chapter, you'll learn how to write simple functions, as well as functions that accept multiple arguments and return multiple values. You'll also have the opportunity to apply these new skills to questions commonly encountered by data scientists.

2. In this chapter, you'll learn to write functions with default arguments so that the user doesn't always need to specify them, and variable-length arguments so they can pass an arbitrary number of arguments on to your functions. You'll also learn about the essential concept of scope.

3. Learn about lambda functions, which allow you to write functions quickly and on the fly. You'll also practice handling errors in your functions, which is an essential skill. Then, apply your new skills to answer data science questions.

"I've used other sites, but DataCamp's been the one that I've stuck with."

Devon Edwards Joseph

Lloyd's Banking Group

"DataCamp is the top resource I recommend for learning data science."

Louis Maiden

Harvard Business School

"DataCamp is by far my favorite website to learn from."

Ronald Bowers

Decision Science Analytics @ USAA

# data[].attributes.attachment_link.description
In this chapter, you'll learn how to write simple functions, as well as functions that accept multiple arguments and return multiple values. You'll also have the opportunity to apply these new skills to questions commonly encountered by data scientists. Learn about lambda functions, which allow you to write functions quickly and on the fly.

# data[].attributes.headline
Online Course: Introduction to Python for Data Science

# data[].attributes.subject
Online Course: Introduction to Python for Data Science

# data[].attributes.attachment_link.readable_text
Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.

In this practical course, you will start from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. Along the way, you'll learn about Python functions and control flow. Plus, you'll look at the world of data visualizations with Python and create your own stunning visualizations based on real data.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

Note: These courses will retire in June. Please enroll only if you are able to finish your coursework in time.

More about this course

Explore Python language fundamentals, including basic syntax, variables, and types

Create and manipulate regular Python lists

Use functions and import packages

Build Numpy arrays, and perform interesting calculations

Create and customize plots on real data

Supercharge your scripts with control flow, and get to know the Pandas DataFrame

Expand what you'll learn

Section 1: Python Basics
Take your first steps in the world of Python. Discover the different data types and create your first variable.

Section 2: Python Lists
Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.

Section 3: Functions and Packages
Learn how to get the most out of other people's efforts by importing Python packages and calling functions.

Section 4: Numpy
Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.

Section 5: Matplotlib
Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.

Section 6: Control flow and Pandas
Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.

Expand syllabus

Pursue a Verified Certificate to highlight the knowledge and skills you gain

# data[].attributes.attachment_link.description
Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science.

# data[].attributes.attachment_link.readable_text
Tried to build some data science projects to improve your resume and got intimidated by the size of the code and the number of concepts used? Does it feel too out of reach, and did it crush your dreams of becoming a data scientist? We have collected for you sixteen data science project ideas with source code so you can actually participate in the fundamentals of data science. These will help boost confidence and also tell the interviewer that you're serious about data science.
Do you know?
Finding a perfect idea for your project is something that concerns you more than implementing the project itself, isn't it? So keeping the same in mind, we have compiled a list of over 500+ project ideas just for you. All you have to do is bookmark this article and get started.

1. Python Project Ideas

2. Python Django (Web Development) Project Ideas

3. Python Game Development Project Ideas

4. Python Artificial Intelligence Project Ideas

5. Python Machine Learning Project Ideas

6. Python Data Science Project Ideas

7. Python Deep Learning Project Ideas

8. Python Computer Vision Project Ideas

9. Python Internet of Things Project Ideas

In this blog, we will list out different data science project examples in the languages R and Python. Let's separate these on the basis of difficulty so you have a proper path to follow.

Top Data Science Project Ideas

Here are the best data science project ideas with source code:

1. Beginner Data Science Projects

1.1 Sentiment Analysis

Sentiment analysis is the act of analyzing words to determine sentiments and opinions that may be positive or negative in polarity. This is a type of classification where the classes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted,..). We'll implement this data science project in the language R and use the dataset by the 'janeaustenR' package. We will use general-purpose lexicons like AFINN, bing, and loughran, perform an inner join, and in the end, we'll build a word cloud to display the result.

Language: R

Dataset/Package: janeaustenR

1.2 Fake News Detection

A king of yellow journalism, fake news is false information and hoaxes spread through social media and other online media to achieve a political agenda. In this data science project idea, we will use Python to build a model that can accurately detect whether a piece of news is real or fake. We'll build a TfidfVectorizer and use a PassiveAggressiveClassifier to classify news into "Real" and "Fake". We'll be using a dataset of shape 7796×4 and execute everything in Jupyter Lab.

Language: Python

Dataset/Package: news.csv

1.3 Detecting Parkinson's Disease

We have started using data science to improve healthcare and services - if we can predict a disease early, it has many advantages on the prognosis. So in this data science project idea, we will learn to detect Parkinson's Disease with Python. This is a neurodegenerative, progressive disorder of the central nervous system that affects movement and causes tremors and stiffness. This affects dopamine-producing neurons in the brain and every year, it affects more than 1 million individuals in India.

Language: Python

Dataset/Package: UCI ML Parkinsons dataset

1.4 Color Detection

How many times has it occurred to you that even after seeing, you don't remember the name of the color? There can be 16 million colors based on the different RGB color values but we only remember a few. So in this project, we are going to build an interactive app that will detect the selected color from any image. To implement this we will need a labeled data of all the known colors then we will calculate which color resembles the most with the selected color value.

Language: Python

Dataset: Codebrainz Color Names

2. Intermediate Data Science Projects

2.1 Speech Emotion Recognition

Let's learn to use different libraries now. This data science project uses librosa to perform Speech Emotion Recognition. SER is the process of trying to recognize human emotion and affective states from speech. Since we use tone and pitch to express emotion through voice, SER is possible; but it is tough because emotions are subjective and annotating audio is challenging. We'll use the mfcc, chroma, and mel features and use the RAVDESS dataset to recognize emotion on. We'll build an MLPClassifier for the model.

Language: Python

Dataset/Package: RAVDESS dataset

2.2 Gender and Age Detection

This is an interesting data science project with Python. Using just one image, you'll learn to predict the gender and age range of an individual. In this, we introduce you to Computer Vision and its principles. We'll build a Convolutional Neural Network and use models trained by Tal Hassner and Gil Levi for the Adience dataset. We'll use some .pb, .pbtxt, .prototxt, and .caffemodel files along the way.

Language: Python

Dataset/Package: Adience

2.3 Uber Data Analysis

This is a data visualization project with ggplot2 where we'll use R and its libraries and analyze various parameters like trips by the hours in a day and trips during months in a year. We'll use the Uber Pickups in New York City dataset and create visualizations for different time-frames of the year. This tells us how time affects customer trips.

Language: R

Dataset/Package: Uber Pickups in New York City dataset

2.4 Driver Drowsiness detection

Drowsy driving is extremely dangerous and around thousands of accidents happen each year due to drivers falling asleep while driving. In this Python project, we will build a system that can detect sleepy drivers and also alert them by beeping alarm.

This project is implemented using Keras and OpenCV. We will use OpenCV for face and eye detection and with Keras, we will classify the state of the eye (Open or Close) using Deep neural network techniques.

2.5 Chatbot

Chatbots are an essential part of the business. Many businesses has to offer services to their customers and it needs a lot of manpower, time and effort to handle customers. The chatbots can automate most of the customer interaction by answering some of the frequent questions that are asked by the customers. There are mainly two types of chatbots: Domain-specific and Open-domain chatbots. The domain-specific chatbot is often used to solve a particular problem. So you need to customize it smartly to work effectively in your domain. The Open-domain chatbots can be asked any type of question so it requires huge amounts of data to train.

Language: Python

Dataset: Intents json file

2.6 Handwritten Digit Recognition

The MNIST dataset of handwritten digits is widespread among the data scientists and machine learning enthusiasts. It is an amazing project to get started with the data science and understand the processes involved in a project. The project is implemented using the Convolutional Neural Networks and then for real-time prediction we also build a nice graphical user interface to draw digits on a canvas and then the model will predict the digit.

Language: Python

Dataset: MNIST

3. Advanced Data Science Projects

3.1 Image Caption Generator

Describing what's in an image is an easy task for humans but for computers, an image is just a bunch of numbers that represent the color value of each pixel. So this is a difficult task for computers to understand what is in the image and then generating the description in Natural language like English is another difficult task. This project uses deep learning techniques where we implement a Convolutional neural network (CNN) with Recurrent Neural Network( LSTM) to build the image caption generator.

Dataset: Flickr 8K

Language: Python

Framework: Keras

3.2 Credit Card Fraud Detection

By now, you've begun to understand the methods and concepts. Let's move on to some advanced data science projects. In this project, we'll use R with algorithms like , Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier. We'll use the Card Transactions dataset to classify credit card transactions into fraudulent and genuine. We'll fit the different models and plot performance curves for them.

Language: R

Dataset/Package: Card Transactions dataset

3.3 Movie Recommendation System

In this data science project, we'll use R to perform a movie recommendation through machine learning. A recommendation system sends out suggestions to users through a filtering process based on other users' preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A - they might like it too. This keeps customers engaged with the platform.

Language: R

Dataset/Package: MovieLens dataset

3.4 Customer Segmentation

Customer Segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits so they can market to each group effectively. We'll use and also visualize the gender and age distributions. Then, we'll analyze their annual incomes and spending scores.

Language: R

Dataset/Package: Mall_Customers dataset

3.5 Breast Cancer Classification

Coming back to the medical contributions of data science, let's learn to detect breast cancer with Python. We'll use the IDC_regular dataset to detect the presence of Invasive Ductal Carcinoma, the most common form of breast cancer. It develops in a milk duct invading the fibrous or fatty breast tissue outside the duct. In this data science project idea, we'll use and the Keras library for classification.

Language: Python

Dataset/Package: IDC_regular

3.6 Traffic Signs Recognition

Traffic signs and rules are very important that every driver must follow to avoid any accident. To follow the rule one must first understand how the traffic sign looks like. A human has to learn all the traffic signs before they are given the license to drive any vehicle. But now autonomous vehicles are rising and there will be no human drivers in the upcoming future. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. The German Traffic signs recognition benchmark dataset (GTSRB) is used to build a Deep Neural Network to recognize the class a traffic sign belongs to. We also build a simple GUI to interact with the application.

Language: Python

Dataset: GTSRB (German Traffic Sign Recognition Benchmark)

Summary

The code to all these data science project ideas is available to you on DataFlair. Get started now and build a project in Data Science. Follow from beginner to advanced, and once you're done, you can move on to other projects.

# data[].attributes.attachment_link.title
16 Data Science Project Ideas with Source Code to Strengthen your Resume - DataFlair

# data[].attributes.attachment_link.description
Tried to build some data science projects to improve your resume and got intimidated by the size of the code and the number of concepts used? Does it feel too out of reach, and did it crush your dreams of becoming a data scientist?