How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
What is the basis for the Data Science course and Data Scientist to know?
1-Algorithm
2-Data
3-Ask The Right Question
4-Predict an answer
5- Copy other people's work to do data science
Claudia Gold: Learning Data Science Onlinesfdatascience
Claudia Gold, author of the Data Analysis Learning path on SlideRule, talks about why she wrote it and how to approach learning data science on your own. https://www.mysliderule.com/learning-paths/data-analysis/
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
What is the basis for the Data Science course and Data Scientist to know?
1-Algorithm
2-Data
3-Ask The Right Question
4-Predict an answer
5- Copy other people's work to do data science
Claudia Gold: Learning Data Science Onlinesfdatascience
Claudia Gold, author of the Data Analysis Learning path on SlideRule, talks about why she wrote it and how to approach learning data science on your own. https://www.mysliderule.com/learning-paths/data-analysis/
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
Clare Corthell: Learning Data Science Onlinesfdatascience
Clare Corthell, Data Scientist and Designer at Mattermark, and author of the Open Source Data Science Masters, shares her experience teaching herself data science with online resources. http://datasciencemasters.org/
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
Interleaving, Evaluation to Self-learning Search @904LabsJohn T. Kane
Presented at Open Source Connections Haystack Relevance Conference on 904Labs' "Interleaving: from Evaluation to Self-Learning". 904Labs is the first to commercialize "Online Learning to Rank" as a state-of-art for technical Self-learning Search Ranking that automatically takes into account your customers human behaviors for personalized search results.
Becoming a Data Scientist: Advice From My Podcast GuestsRenee Teate
Information and advice about learning data science, from the 17 data scientists & data science learners I have interviewed to date on the Becoming a Data Scientist Podcast, and from me!
Originally presented at PyDataDC conference, 10/9/2016
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
데이터 과학자의 실체 The Reality of Data Scientist
전체 분석 과정에서 대부분은 데이터를 모으고 가공하는데 소요한다.
그리고 애플리케이션에 데이터를 적용하기 위해서는 테스팅이 가장 중요하다.
인간공학 전공자들을 대상으로 준비한 발표자료라서 '데이터 수집 및 클렌징'보다는 '테스트 (온라인 테스트)'에 초점을 두고 자료를 만들었습니다.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Clare Corthell: Learning Data Science Onlinesfdatascience
Clare Corthell, Data Scientist and Designer at Mattermark, and author of the Open Source Data Science Masters, shares her experience teaching herself data science with online resources. http://datasciencemasters.org/
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
Interleaving, Evaluation to Self-learning Search @904LabsJohn T. Kane
Presented at Open Source Connections Haystack Relevance Conference on 904Labs' "Interleaving: from Evaluation to Self-Learning". 904Labs is the first to commercialize "Online Learning to Rank" as a state-of-art for technical Self-learning Search Ranking that automatically takes into account your customers human behaviors for personalized search results.
Becoming a Data Scientist: Advice From My Podcast GuestsRenee Teate
Information and advice about learning data science, from the 17 data scientists & data science learners I have interviewed to date on the Becoming a Data Scientist Podcast, and from me!
Originally presented at PyDataDC conference, 10/9/2016
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
데이터 과학자의 실체 The Reality of Data Scientist
전체 분석 과정에서 대부분은 데이터를 모으고 가공하는데 소요한다.
그리고 애플리케이션에 데이터를 적용하기 위해서는 테스팅이 가장 중요하다.
인간공학 전공자들을 대상으로 준비한 발표자료라서 '데이터 수집 및 클렌징'보다는 '테스트 (온라인 테스트)'에 초점을 두고 자료를 만들었습니다.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
How to crack Big Data and Data Science rolesUpXAcademy
How to crack Big Data and Data Science roles is the flagship event of UpX Academy. This slide was used for the event on 10th Sept that was attended by hundreds of participants globally.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
One of the most popular buzz words nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations to seek experts who can implement Machine Learning into their businesses.
The paper will be written for statistical programmers who want to explore Machine Learning career, add Machine Learning skills to their experiences or enter a Machine Learning fields. The paper will discuss about personal journey to become to a Machine Learning Engineer from a statistical programmer. The paper will share my personal experience on what motivated me to start Machine Learning career, how I started it, and what I have learned and done to be a Machine Learning Engineer. In addition, the paper will also discuss the future of Machine Learning in Pharmaceutical Industry, especially in Biometric department.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
Overview of Data Science Courses Online
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
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Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
Build a data analysis pipeline, from collection to analysis to presenting data visually.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
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Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
data science online training in hyderabadVamsiNihal
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge. Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
From SQL to Python - A Beginner's Guide to Making the Switch
1. FROM SQL TO PYTHON:
HANDS-ON DATA
ANALYTICS AND MACHINE
LEARNING
RACHEL BERRYMAN
DATA SCIENTIST – TEMPUS ENERGY
RACHELKBERRYMAN@GMAIL.COM
2. ABOUT ME
MSc Sustainable
Development, BA
Economics
Senior Energy Data
Analyst
Data Science Retreat
Batch 12, 2017
Data Scientist at
Tempus Energy.
Instructor of Model
Pipelines course at DSR
3. FROM DATA ANALYSIS TO DATA SCIENCE
What’s the
difference between
data analysis and
Data Science?
How do I know if a
career in Data Science
is right for me?
How do I make the
switch to a career in
Data Science?
4. WHAT IS DATA SCIENCE?
• Data Science definition (Wikipedia): Data
Science is a concept to unify statistics,
data analysis, machine learning and their
related methods in order to understand
and analyze actual phenomena with data.
It employs techniques and theories
drawn from many fields within the broad
areas
of mathematics, statistics, information
science, and computer science.
5. DATA ANALYSIS VS. DATA SCIENCE:
WHAT’S THE DIFFERENCE?
• Data Analysis mainly looks at the present and past. It answers
questions like:
• How much revenue did we bring in last year?
• What is product does customer X buy most frequently?
• Data Science mainly looks at the present and future. It answers
questions like:
• What products should we invest in expanding for the future?
• What product should we recommend to customer X so that they buy
more when they visit our site, based on their most-purchased products
in the past?
6. DATA ANALYSIS VS. DATA SCIENCE:
WHAT’S THE DIFFERENCE?
• Data Analysis works mainly with proprietary tools
• Oracle, MS SQL Server, Tableau.
• Data Science works mainly with open-source tools
• Open-source languages and packages: ie Python, scikit-learn, keras,
matplotlib
7. DATA ANALYSIS VS. DATA SCIENCE:
WHAT’S THE DIFFERENCE?
• Data Analysis works with data from one or few sources
• Ex: data from an in-house SQL database.
• Data Science works with data from many varying sources
• Ex: data from in-house SQL database, data scraped from the web, text
data from customer surveys, data from across multiple departments
8. WHY DO COMPANIES NEED DATA
SCIENTISTS?
• More and more data comes in
unstructured formats: ex,
natural language in emails and
social media posts, photos,
audio files.
• Data Scientists make use of
this data and apply it to
pressing business questions.
9.
10. IS DATA SCIENCE RIGHT FOR ME?
You like to code
You like to work across many teams and find synergies
You enjoy getting “stuck in” and working through challenges
You consider yourself a life-long learner
You like math
You seek out work and answers to questions: you don’t wait
for questions to be delegated to you
11. HOW CAN I MAKE THE SWITCH FROM DATA
ANALYSIS TO DATA SCIENCE?
• Learn the basics:
• Practical
• Command Line
• Git and Github
• A common Data Science programming language (Python, or R).
• Theoretical
• Machine Learning Algorithms
• Supervised, Unsupervised
• Go further:
• MOOCs
• Intensive deep-dive: Bootcamp/Retreat
12. LEARN THE BASICS: COMMAND LINE & GIT
• Command line is how you directly interact with your computer
(sans GUI). It is the “ultimate seat of power for your computer”.
• Git is a distributed version control system. Git is responsible for
keeping track of changes to content (usually source code files),
and it provides mechanisms for sharing that content with
others. GitHub is a company that provides Git repository
hosting.
• Learn how to use your command line to clone repositories
(‘repos’) from GitHub. You will open up a world of learning
opportunities!
13. LEARN THE BASICS: PYTHON FOR DATA
MUNGING AND ANALYSIS
• In SQL, you’re usually using a company-purchased software
like Oracle SQL Developer, or MS SQL Server Management
Studio.
14. LEARN THE BASICS: PYTHON FOR DATA
MUNGING AND ANALYSIS
Python
Interpreter
iPython IDE/Jupyter
Get
Coding!
15. LEARN THE BASICS: PYTHON FOR DATA
MUNGING AND ANALYSIS
Python Interpreter
17. LEARN THE BASICS: PYTHON FOR DATA
MUNGING AND ANALYSIS
Jupyter Notebook
18. LEARN THE BASICS: PYTHON FOR DATA
MUNGING AND ANALYSIS
• IDEs:
• Python-specific:
• PyCharm
• Spyder
• Thonny
• General, with support for
Python:
• Atom (also can add iPython
with Hydrogen)
• Sublime Text
• Vim
22. LEARN THE BASICS: PYTHON FOR DATA
MUNGING AND ANALYSIS
• Start with what you know!
• Write SQL commands in Python
• Automate what you would have to do manually in Excel
• Use matplotlib to make visualizations you would have done in Tableau
• Learn what you don’t know
• Python packages, modules, libraries
• Object-Oriented Programming (OOP)
23. LEARN THE BASICS: PYTHON FOR DATA
SCIENCE AND MACHINE LEARNING
• Github Repo with practice for switching from SQL to Python
• Clone repo: https://github.com/rachelkberryman/From_SQL_to_Python
• cd into repo, and run command “jupyter notebook” (more information
about jupyter notebooks here)
• Includes:
• sample SQL queries with Python equivalents
• examples of Python functions for data manipulation and analysis
• sample visualizations in Seaborn
24. LEARN THE BASICS: PYTHON FOR DATA
SCIENCE AND MACHINE LEARNING
• By applying machine learning algorithms (with code), you will
learn them MUCH more quickly than by only reading about
them
• Even better, apply them to a sample dataset (work and/or
passion project)
25. GOING FURTHER: MOOCS VS. DEEP DIVE
• The jump from automating what you already know to working
with predictive models is where most people get overwhelmed.
• Need for more structured learning: MOOCs vs. Deep Dive
26. GOING FURTHER: MOOCS VS. DEEP DIVE
MOOCs:
• PROs:
• On your own time
• Little risk
• CONs:
• No or little help when stuck
• No supportive community/job
help
Deep Dive:
• PROs:
• Structure and support
• Faster learning rate
• Network and hiring support
• CONs:
• Risk and opportunity cost
27. MAKING THE SWITCH: GOING FURTHER
• MOOCs:
• Andrew Ng’s Machine Learning course on Coursera
• Explanation of machine learning algorithms
• Applied Data Science with Python course on Coursera
• Coding practice in notebooks, with explanation videos
• Deep Dive: Data Science Retreat
• 3 months of intensive data science teaching and training in Berlin
• Culminates in final portfolio project
28. MORE RESOURCES
• On Python:
• Think Python: Thinking Like a Computer Scientist, Allan B. Downey
• Fluent Python, Luciano Ramalho
• On DS/ML:
• Think Bayes: Bayesian Statistics in Python, Allan B. Downey
• The Master Algorithm, Pedro Domingos
• Pattern Recognition in Machine Learning, Christopher Bishop
SaaS platform for energy utility bills: got data in a lot of formats, and basically just fit it into the Database. Creative bit was getting to use tableau. Quickly realized I wanted to work on more intense analytics/topics.
Thrown in the deep end at DSR, but I survived.
To process all of this unstructured data, you need to not only analyze it and be able to make predictions about what it will do in the future, but you also build solutions for managing it, storing it, and manipulating it. This is why many people see Data Scientists as first and foremost being Software Engineers.
Source: Wikipedia.
DS: we don’t just query the data that’s already there, we use that data to create predictions for the future.
Could be argued, but I’ve found this to be true in industry. Proprietary tools cost money, and you’re (mostly) bound to only the data your company has.
- Life long learner: because it’s such a new field, there are constantly new technologies and libraries coming out that you have to stay up on.
Command line is ESSENTIAL before learning any “proper” coding language.
https://www.davidbaumgold.com/tutorials/command-line/
https://softwareengineering.stackexchange.com/questions/173321/conceptual-difference-between-git-and-github
Git is a revision control system, a tool to manage your source code history.
GitHub is a hosting service for Git repositories.
So they are not the same thing: Git is the tool, GitHub is the service for projects that use Git.
It’s important to learn how different working in Python is from working in SQL.
In SQL, you’re usually using a company-purchased software like Oracle SQL Developer, or MS SQL Server Management Studio.
With these, you usually have authentications that let you run queries on various databases in the RDBMS.
Because SQL is a QUERY language, it’s only as good as the data you have access to.
The great thing about Python is that you’re not tied to one buggy editor for running your code. Also, you’re not tied to data from one source.
Python isn’t like this. Anyone can code in python if they have a computer, you just have to know how to get it started! To start learning Python, you have to get it working on your computer.
To code in Python you have to have a python interpreter on your computer. This is the program that reads your python code and does what it says. Macs have this built in.
iPython is an interface to the python language. It lets you run small bits of code without writing entire programs. Usually, regular Python is used for scripts that you’ve already written.
A script contains a list of commands to execute in order. It runs from start to finish and display some output. On the contrary, with IPython, you generally write one command at a time and you get the results instantly, and it has a lot of features to make it work better.
Additional features:
Tab autocompletion (on class names, functions, methods, variables)
More explicit and colour-highlighted error messages
Better history management
Basic UNIX shell integration (you can run simple shell commands such as cp, ls, rm, cp, etc. directly from the IPython command line)
Another interface, but web-based. Makes it easy to share as you can save them as HTML files or PDFs. Lets you both run code (via an ipython kernel), and add text in markdown cells. Great for playing around and trying things.
IDE (or Integrated Development Environment) is a program dedicated to software development. (ideally) lets you work in both script and interactive modes. Good for when you’ve “graduated” from jupyter notebooks and need something to right more full length programs.
Anaconda is a distribution of Python, made specifically for data science. The goal is to make it easy to have everything you need to do data science in python. When you download it, you automatically get jupyter, and a lot of the core libraries. Now, about libraries…
There are a lot of libraries in python that directly deal with data: Ex: pandas
There are also a LOT that don’t.
Learn the ones that deal with data first! Also, learn the ones that deal with data that you already know how to work with first. Ex: learn pandas for working with CSVs before you learn beautifulsoup for scraping data off the web.
Quick read on some of this biggest Python libraries: http://www.developintelligence.com/blog/python-ecosystem-2017/
When I started with python, I was frustrated not being able to do everything I could already do in SQL, as far as manipulating data.
A lot of the tutorials are abstract and go too far in to the basics.
Once you have Python working, you can start using it on a real dataset. This is e-commerce data from Kaggle.
Once you’ve played around a bit with the SQL-like and data-focused libraries, you can move on to learning about machine learning, and going beyond just analytics.
Get a rough idea, ex: If yours is a supervised or unsupervised learning problem, if it’s regression or classification, and then, read about each algorithm as you implement them.
