Python for Data Science – Bootcamp with Job Guarantee!

Python is a general-purpose programming language that is becoming very popular for data science, machine learning, deep learning and even software development.

Practitioners thought-out the world are using Python to harvest insights from the data to help business gain a competitive edge.

Python is simple and easy to learn, making it very approachable for beginners and experts in Data Science because of a welcoming and established community.

I love Python because it is powerful and versatile.

In this piece, my goal is to simply suggest the resources that will equip and edify you to master key concepts in Python for data science from the field ion to cultivate life long learning agility.

If you are a beginner and want to Learn how to learn data science, I’d suggest that you give this article “Become a Data Scientist,” a read.

However, As a busy professional, you may be looking for something that is very concise and fast. I’ve got you some excellent resources for you.

One about Learning Probability and Statistics for Data Science and one about Learning Maths for Data Science and Machine Learning.

Now, without further ado, let’s get started!

 


11+ Python for Data Science Courses from World-Class Educators

These classes will give you a sense of the data science education and help you cultivate Pythonic intuition, you’ll need to be effective in your data science learning.

Also, I don’t assume basic comfortability with any Programming language, Statistics and Mathematics but if you do, you will surely get ahead.

 


Data Science Bootcamp – job guarantee! SpringBoard

Get a job, or your money back, through Springboard’s mentor-guided online data science bootcamp. With Springboard’s job guarantee, if you don’t land a job within six months of graduating, your tuition will be refunded.

Throughout the course, students will work 1:1 with an expert data science mentor to master the data science process, from statistics and data wrangling to advanced topics like machine learning and data storytelling, by working on real projects designed by industry experts.

With the guidance of your personal mentor and career coaches, you will graduate with an interview-ready portfolio and a network of data scientists. Students are supported every step of the way—until they get hired.

Springboard has already helped hundreds of students land jobs at top tech companies (including Facebook, Google, Salesforce, and Amazon) while increasing students’ salaries by an average of $25,700.

Data Science Career Track

Is it right for you?

The Data Science Career Track was designed for people with prior experience in statistics and programming, such as software developers, analysts, and finance professionals. All professional and academic backgrounds are welcome.

Upon completing the course, students will have the hands-on experience, portfolio, and skills necessary to land a data science role.

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Introduction to Python DataCamp

Introduction to Python for Data Science course is taught by Hugo Bowne-Andreson and delivered via DataCamp.

DataCamp LogoThis Introductory level course in Python focuses specifically on data science and aims to equip learners with the most basic but important data science concepts in python.

First, you will learn the basic concepts of Python and powerful ways to store and manipulate data.

Next, you will learn about the helpful data science tools to begin conducting your own analysis.

Finally, you will learn to use numpy, a package used for scientific computing and also understand the key concepts data exploration through hands-on exercise.

Introduction to Python

Is it right for you?

This course is suitable for beginners who have no background in programming or understanding of statistics.

Upon the successful completion of this course and all exercises, you will have gained the basic and important knowledge of data types used in Python for data science.

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Python for Data Science & AI IBM

IBM LogoThis Introductory course in Python for Data Science is designed by IBM and taught by Joseph Santarcangelo, who is a highly skilled Data Scientist working with IBM.

This course is deigned to help learners gain functional programming skills and bootstrap your knowledge in Python for data science.

First, you will deeply learn about the Python basics and through the guided lectures learn types, expressions, variables and string operations. You will also learn more about the use of data structures and practice exercises on lists, tuples, dictionaries and sets.

Furthermore, you will dig deeper to understand the fundamentals of Python programming and also learn the important topics (with practice exercises) like; Conditioning and Branching, Loops, Functions, Objects and Classes.

Next, you will go through the series of lectures and practice exercises to learn working with data in python. This module is very important for beginners to understand Reading files with Open, Writing files with Open, Loading, Saving Data with Pandas and also working with it.

Moreover, you will also learn One Dimensional Numpy and Two Dimensional Numpy in this module, along with an introduction to Simple APIs.

Finally, after watching the series of lectures and devoting your time in practice exercises, you will create a project to test your skills.

Python for Data Science and AI - IBM

Is it right for you?

This course is suitable for beginners and is offered in English with subtitles in Korean and German.

By the end of this course, you will have acquired descent knowledge of Python for data science and functional skills in Pandas and Numpy to perform data analysis.

This course is part of multiple programs.

  • Applied Data Science Specialization
  • IBM Applied AI Professional Certificate
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Introduction to Data Science in Python DataCamp

DataCamp LogoThis Interactive course is designed by Hillary Green-Lerman, a Lead Data Scientist at Looker and delivered via DataCamp.

Introduction to Data Science in Python aims to help learns to better understand how to effectively and efficiently analyze the Data.

First, you will receive a general introduction to data analysis in Python and cover the lectures with hands-on exercise on Python syntax, loading modules and making use of functions to get a suspect list for the kidnapping of Bayes, the Golden retriever.

Next, you will dive into loading Data in Pandas and examine credit card records for the suspects and find patterns to see any of them make suspicious purchases.

Furthermore, you will learn to about plotting data with matplotlib and understand the techniques to comprehend in a better way, by analyzing the letter frequencies from the ransom note and several handwriting samples to determine the kidnapper.

Finally, you will use the different types of Plots; scatter plots, bar plots, and histograms. You will make use of these tools to locate where the kidnapper is hiding and rescue Bayes.

data science in python course

Is it right for you?

This course is suitable for beginners, although little knowledge of python and statistics will be very helpful to complete this course with ease.

This course is also part of Data Analyst with Python Track.

Upon the successful completion of this course, you will be ready to take any Intermediate Data Science Course in Python

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Python 3 Basics University of Michigan

The University of MichiganPython 3 Basics is offered by the University of Michigan and delivered via Coursera. This course introduces the learners to the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures.

In this course, you will program an on-screen Turtle to draw pretty pictures and learn to draw reference diagrams as a way to reason about program executions, which will help to build up and even improve your debugging skills.

First, you will receive a general introduction in Python through lectures and the Runestone textbook. This module will cover the basics to help you write your first python program and also you will learn to draw images by writing a program.

Next, you will dig deeper into Sequences and Iteration to understand the basics of a few Python data types – strings, lists, tuples, loops and more. And in this module, you will also learn to write more complex programs that create drawings by incorporating for loops.

Furthermore, you will cover very important topics to learn Binary, Unary, Nested, and chained conditions as well as how to incorporate conditionals within an accumulation pattern.

Finally, you will gain deeper knowledge on using lists, strings and Python objects in the “Sequence Mutation and Accumulation Patterns,” module.

python 3 basics

Is it right for you?

There are no prerequisites for taking this course and is perfectly suitable for a newcomer to Python programming.

Upon the completion of the lectures and use of Runestone textbook, you will test your knowledge and skills through application in final assessment, with a little more difficult set of tasks.

This course is part and first of five courses in the Python 3 programming Specialization. ( Check below ! )

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Intermediate Python for Data Science DataCamp

Intermediate Python for Data Science is offered on DataCamp by a Data Science Instructor, Filip Schouwenaars, who has published several high-rated courses, covering both R and Python.

python dats science course This course is highly recommended for any aspiring data science practitioner to consolidate their knowledge and skills by blending together everything they learn in this course to solve a case study using hacker statistics.

First, you will learn to visualize real data with Matplotlib’s functions and build various types of plots, and customize them to be more visually appealing and interpretable.

Next, you will learn about dictionaries to get acquainted with data structures and dig deeper with hands-on practice to learn the pandas DataFrame.

Finally, after learning key concepts such as boolean logic, control flow, and loops in Python, you will apply all the concepts you have learned in this course using hacker statistics to calculate your chances of winning a bet.

Moreover, you will use random number generators, loops and Matplotlib to gain a competitive edge!

Intermediate Python for Data Science Course

Is it right for you?

This course is suitable for learners with basic understanding of Python for Data Science.

By the end of this course, you will be equipped to create your own data visualizations from the datasets available on the internet and also highly prepared to take any advanced data science or machine learning courses.

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Importing Data in Python [ part 1 ] DATACAMP

This is another excellent course by Hugo Browne-Andreson.

DataCamp LogoIn this course, “Importing Data in Python,” you’ll learn the many ways to import data into Python from various sources, such as Excel spreadsheets, SAS, STATA, Matlab Files, relational databases and right from the web.

First, you’ll learn how to import data into Python from all types of flat files and If you have previously learned NumPy and pandas, then you will learn how to use these packages to import flat files and customize your imports.

Next, You’ll learn many other file types you will potentially have to work with as a data scientist. In this module, you’ll learn how to import data into Python from a wide array of important file types such as pickled files, Excel, SAS and Stata files, HDF5 files, Files for Numerical data, and MATLAB files.

In the final module, you’ll work with relational databases in Python and learn to extract meaningful data from them. You will also learn about relational models, create SQL queries, filter and order your SQL records and also perform advanced queries by joining database tables.

python data science course

Is it right for you?

This course is suitable for learners with background in programming, statistics and intermediate comfortability with relational databases.

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Importing Data in Python [ part 2 ] IBM

This interactive course is also designed by Hugo Browne-Anderson from DataCamp.

DataCamp LogoThis course simply aims to help learners improve their Python Data Importing Skills to work with Web and API data.

First, you will learn how to get data from the web, whether it is stored in files or in HTML.

Moreover, you’ll learn the basics of web scraping and parsing web data.

In the second module, you will gain a deeper understanding of how to import data from the web. You will also learn the basics of extracting data from APIs and practice extracting data from publicly available APIs.

Finally, Through the guided lectures, you will learn how to stream real-time Twitter data, and how to Analyze and Visualize it.

python data science course

Is it right for you?

This course is suitable for learners who have intermediate comfortability in Python.

These courses are part of the following Learning/ Skill tracks

By the end of this course, you will have acquired life-long skills that are very valuable.

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Applied Data Science Specialization IBM

IBM LogoApplied Data Science Specialization is comprised of 4 courses, designed by IBM Technical Instructors and delivered via Coursera.

This specialization aims to equip learners to go from zero to expert level and acquire skills for solving real world data problems.

First, you will learn the basics of Python for Data Science and AI. You will also learn to use Numpy and Pandas through guided lectures, hands-on exercises and projects to test your skills.

Next, you will dig deeper to perform some Data Analysis with Python and apply the skills you learned in first course of this specialization. You will also learn to perform statistical analysis and predict the future trends from the data. Data Analysis course is perfect place to learn Sci-kit learn which will be good for your Machine Learning Skills.

Applied Data Science Specialist Furthermore, you will learn from the World-Class Instructors to create meaningful Data Visualizations in Python. You will learn from the various techniques to make good use of data visualization libraries in Python, namely Seaborn, Matplotlib, and also Folium.

Finally, you will devote your time in applying your data science skills in a capstone project. In this course, you will make use of location data from different location data providers and heavily invest your time in using RESTFUL API calls to Foursquare API to retrieve data from the location data points.

Appllied Data Science Specialization

Is it right for you?

This course is suitable for beginners – no prior programming knowledge is necessary. However, basic understand of statistics will be helpful to get ahead.

You will love this MOOC, it is packed with hands-on labs exercises and you will dive deeper into geospatial data to communicate your results and findings.

Upon the successful completion of this course, you will have acquired lifelong Python skills and also recognition as a Specialist in Applied Data Science from IBM.

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Unit Testing for Data Science in Python DATACAMP

This Interactive course is designed by Dibya Chakravorty, who is a Senior Python developer at TECH-5.

DataCamp LogoIn this course, you will learn how to write unit tests, interpret the test result reports and fix bug for your Data Science projects in Python. This might actually change the way you code forever!

First, you will learn about the pytest package and write simple unit tests. You will make use of examples from the data preprocessing module of a linear regression to learn unit testing in the context of data science.

In second module, you will learn to write advanced unit tests using Numpy arrays for testing exception handling. You will also get introduced to Test Driven Development (TDD) put it to practice.

Furthermore, you will learn about how to structure your test suite, execute any subset of tests and mark problematic tests so that your test suite is streamlined. You will also learn to add the trust-inspiring build status and code coverage badges for you projects.

Finally, you will gain valuable unit testing skills like setup, teardown and mocking. You will also consolidate your knowledge by writing sanity tests for your data science models and learn to test Matplotlib plots.

Unit Testing for Data Science in Python Course

Is it right for you?

This course is suitable for learners who have intermediate comfortability in Python.

By the end of this course, you will be highly prepared to test real world data science projects.

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Python Data Science Toolbox [ part 1 ] DataCamp

This interactive course from DataCamp is designed by a Hugo Browne Anderson, who is a data scientist, educator, writer and podcaster at DataCamp.

DataCamp LogoThis course is very helpful for any data science practitioners to learn the art of writing their own functions in Python, as well as key concepts like scoping and error handling.

Through the lectures and hands-on exercises, you will gain insight into scoping in Python and learn to write lambda functions and also understand the techniques to handle errors in your function writing practice.

You will acquire new skills in each chapter to write functions that analyze Twitter DataFrames.

python data science toolbox 1

Is it right for you?

This course is suitable for intermediate learners who have basic knowledge of Python and also ability to write scripts and functions.

Upon the completion of this course, you will be able to write custom functions, complete them with multiple parameters and multiple return values, along with default arguments and variable-length arguments.

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Python Data Science Toolbox [ part 2 ] DataCamp

This is another interactive course designed by Hogo Browne-Ansderson from DataCamp.

DataCamp LogoIn this second part of Python Data Science Toolbox course, you will to continue to build your modern Data Science skills by learning about iterators and list comprehensions.

First, you will learn about iterators by using some handy functions that will allow you to effectively work with them and also objects you have already encountered in the context of For loops.

Next, you will learn about list comprehensions and dive into generators, which are very helpful when working with large sequences of data that you may not wish to store in memory, but instead generate on the fly.

Finally, you will work through a case study in which you’ll apply all the techniques you will have acquired toward wrangling and extracting meaningful information from a real-world dataset.

python data science toolbox 2 course

Is it right for you?

This course is suitable for learners who have an intermediate comfortability in Python programming.

These courses are part of the following learning and skill tracks offered by DataCamp;

By the end of these two courses, you will have become skilled with an intuition to solidify your Python data science chops.

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Foundations of Data Science – K means Clustering in Python University of London 

This course is designed by an academic team from Goldsmiths, University of London and delivered via Coursera.

Foundations of Data Science course simply focuses on basic mathematics, statistics and Python programming skills necessary for data analysis.

First, you will learn get an introduction to data science through some real-world examples and acquire some basic programming skills, necessary for mastering data science techniques.

Next, you will learn the key concepts involved to solving the actual problems through Means and Deviations in Mathematics and Python. Moreover, you will heavily invest your time using Jupyter notebooks and Numpy to learn understand the concepts of data clustering.

Finally, you will learn to implement the principle steps of the K-means algorithm in Python and cover the important lectures and practice exercises to move from One Dimensional Data to Two Dimensional Data.

Furthermore, you will learn Pandas and techniques to make a better use of K-Means to Analyze your Data.

Python Data Science Foundations Course

Is it right for you?

This course is suitable for learners with basic knowledge of programming, statistics and some high-school level mathematics.

Upon the successful completion of this course, you will be prepared to design and execute a whole data clustering workflow and also highly prepared to take advanced data science courses.

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Cleaning Data in Python IBM

DataCamp LogoThis is an excellent course offered by DataCamp, designed by Daniel Chen, who is a Data Science Consultant at lander Analytics.

In the first module, you will dive into Exploring your data with an eye for diagnosing issues such as outliers, missing values, and duplicate rows.

Next, you will learn Tidying data for analysis through guided lectures and gain first-hand experience with reshaping and tidying data using techniques like pivoting and melting.

After that, you will dig deeper into Combining data for analysis. This module is very important to learn how to deal with large dataset that needs to be broken into saperate datasets to facilitate easier storage and sharing. You will also learn the process of combining datasets and understand techniques for cleaning each dataset separately.

In the next module, “Cleaning Data for Analysis,” you will gain lifelong skills for programmatically checking your data for consistency. You will also learn some grittier aspects of data cleaning, string manipulation and pattern matching to deal with unstructured data.

Moreover, you will learn how to deal with missing data, as well as duplicate data.

Finally, you will apply all the techniques you will learn in this course towards tidying a real-world, messy dataset to become highly prepared for working on data science projects using Python.

cleaning data in python course

Is it right for you?

This course is suitable for Intermediate learners who have some experience in programming and good knowledge of Statistics.

This course is part of the following learning and skill tracks offered by DataCamp;

By the end of this interactive course and all the hands-on exercises, you will have become highly equipped with the valuable skills to clean your data in Python.

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Thanks for making it to the end ;)

If you liked this article, I’ve got a few practical reads for you. One about the Statistics for Data Science and one about Data Science Skills that companies are actively seeking.

I’ve also got this Data Science Newsletter that you might be into. I send a tiny email once or twice every quarter with some useful resource I’ve found.

Don’t worry, I hate spam as much as you. ☟

 


 

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