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Programming for Data Science

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Added 3 years ago by tutsnode in Other
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Programming for Data Science (Size: 12.4 GB)
  0 1.4 MB
  1. Explore Data Science Domains and Roles .mp4 24.1 MB
  1. Introduction - Loops to Automate Tasks .mp4 21.1 MB
  1. Introduction - Programming for Data Science CBT Nuggets-3.mp4 144 MB
  1. Introduction -2.mp4 38.4 MB
  1. Introduction -3.mp4 99.7 MB
  1. Introduction .mp4 37.3 MB
  1. Introduction Python Built-in Methods .mp4 46.9 MB
  1. Introduction.mp4 82.9 MB
  1 295.3 KB
  2. Bare Bones Completion.mp4 102.6 MB
  2. Big O Notation .mp4 57.4 MB
  2. Comparison and Logical Operators .mp4 88.8 MB
  2. Complexity Analysis and Memory .mp4 93.5 MB
  2. Functions Review .mp4 81.9 MB
  2. How the Internet Works .mp4 39.2 MB
  2. Install Anaconda macOS .mp4 64 MB
  2. List Review .mp4 107.1 MB
  2. Load and Prepare the Dataset (EDA light) .mp4 104.5 MB
  2. Matplotlib vs Seaborn .mp4 149.4 MB
  2. Primitive & Non-Primitive Data Types, Part 1 Conda Environment and GitHub .mp4 35 MB
  2. R and Python Data Structures Part 1 Vectors .mp4 57.2 MB
  2. What is AI.mp4 131.1 MB
  2 648.4 KB
  2. Programming Styles .mp4 123.8 MB
  2. Python and Math .mp4 81.5 MB
  2. What are Data Structures .mp4 85.2 MB
  2. What is BeautifulSoup .mp4 34 MB
  2. What is Data Science .mp4 95.4 MB
  2. What is Git .mp4 61.2 MB
  2. What is Matplotlib .mp4 161.5 MB
  2. What is Numpy .mp4 52.9 MB
  2. What is Pandas Part 1 .mp4 79.1 MB
  2. What is R and Why Should I Learn it in 2023 .mp4 167.8 MB
  2. What is Streamlit .mp4 81.2 MB
  2. What is a command-line, terminal, and Shell .mp4 137.4 MB
  2. Working with Variables .mp4 42.7 MB
  3. API Authentication.mp4 45.3 MB
  3. Algorithm Comparison .mp4 121.9 MB
  3. Big O Notation and Time Complexity Visualization .mp4 57.1 MB
  3. Data Science Tools .mp4 102.3 MB
  3. Fields in the dataset from Kaggle .mp4 155.4 MB
  3. Getting Started with R and Google Colab .mp4 176.8 MB
  3. Install Anaconda Windows .mp4 29 MB
  3. Leaving Comments .mp4 31.6 MB
  3. List Methods .mp4 71.9 MB
  3. Math Operators .mp4 69.1 MB
  3. Numpy Vs Pandas .mp4 95 MB
  3. OpenAI GPT-3 Language Models.mp4 67.4 MB
  3. Perform Exploratory Data Analysis (EDA) Part II .mp4 116.1 MB
  3. Plotting with Seaborn .mp4 92.9 MB
  3. Primitive & Non-Primitive Data Types, Part 2 Data Types in Jupyter Notebook .mp4 60.9 MB
  3. Python Basic Data Structure Limitations .mp4 124.7 MB
  3. Python Class Objects .mp4 169.4 MB
  3. R and Python Data Structures Part 2 Arrays and Lists .mp4 40.5 MB
  3. The find() Method Part 1 .mp4 91.8 MB
  3. Visual Studio Code .mp4 97 MB
  3. What is GitHub .mp4 62 MB
  3. What is Pandas Part 2 .mp4 71.6 MB
  3. What is Streamlit Community Cloud .mp4 32.3 MB
  3. Writing Functions .mp4 74.4 MB
  3. if Statements Part 1 .mp4 123.5 MB
  3. macOS Terminal, Git for Windows, and Linux Emulators .mp4 80.9 MB
  3 1.2 MB
  4. Basic Linux Commands .mp4 105.6 MB
  4. Boolean Values .mp4 30.7 MB
  4. Create an Online Repo and Push Your Code to GitHub .mp4 84.8 MB
  4. Creating a Completion.mp4 114.6 MB
  4. Creating and Manipulating Arrays .mp4 63.2 MB
  4. Customizing Plots .mp4 82.1 MB
  4. Data Science Development Environments .mp4 83.6 MB
  4. Data Structures Deep Dive .mp4 141.5 MB
  4. Designing an AI Web App .mp4 78.5 MB
  4. Dictionary Review .mp4 53.5 MB
  4. EDA (Exploratory Data Analysis) .mp4 85.6 MB
  4. EDA Dimensions .mp4 66.5 MB
  4. HTML .mp4 45.7 MB
  4. If statements and Functions .mp4 80.1 MB
  4. Numbers Integers and Floats .mp4 43.7 MB
  4. Pandas Data Types .mp4 210.6 MB
  4. Quadratic time .mp4 38.2 MB
  4. R Data Types .mp4 101 MB
  4. The find() Method Part 2 .mp4 129.4 MB
  4. Virtual Environments with Conda .mp4 50.6 MB
  4. Working with Strings .mp4 96 MB
  4. if Statements Part 2 .mp4 88.7 MB
  4 1.2 MB
  4. Perform Exploratory Data Analysis (EDA) Part I .mp4 76.3 MB
  4. R and Python Data Structures Part 3 Data Frames .mp4 30.8 MB
  4. What is ChatGPT and How Does it Work Under the Hood.mp4 35.4 MB
  5. Array Operations, Array Methods and Functions .mp4 67.6 MB
  5. Built-in Python Functions .mp4 77.6 MB
  5. Hosting Datasets for use in Jupyter Notebook .mp4 94 MB
  5. Install Jupyter Notebook .mp4 64.3 MB
  5. String Formatting .mp4 33.7 MB
  5. Understanding Functions .mp4 71.3 MB
  TutsNode.net.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
  5 662.2 KB
  5. CSS .mp4 53.3 MB
  5. Challenge .mp4 74.7 MB
  5. Clean and Manipulate Data .mp4 96.4 MB
  5. Create Projects and Workflows .mp4 83 MB
  5. Dictionary Methods .mp4 53.5 MB
  5. EDA Summary Statistics .mp4 74.5 MB
  5. Factorial time .mp4 132.6 MB
  5. HungryBear Non-production Code .mp4 110.6 MB
  5. Operations and Calculations .mp4 59.3 MB
  5. Prompts and Completions.mp4 185.4 MB
  5. Real-world Notebook .mp4 22 MB
  5. Social Network Analysis Use Case .mp4 118.3 MB
  5. Text Strings and Bools .mp4 36.7 MB
  5. The find_all() Method Part 1 .mp4 135.7 MB
  5. Time Complexity.mp4 71.8 MB
  5. What is Anaconda .mp4 48.4 MB
  5. for Loops .mp4 58.4 MB
  6. Bonus Use Case White Paper Summarization.mp4 78.8 MB
  6. Challenge .mp4 28.9 MB
  6. Coffee Shop Complexity .mp4 109.6 MB
  6. Collections Lists .mp4 43.3 MB
  6. Data Science Roles .mp4 43.4 MB
  6. Data Visualization with Pandas (it does that also!) .mp4 103.3 MB
  6. EDA Complete with Histograms .mp4 60.2 MB
  6. HungryBear Production Code Part 1 .mp4 64 MB
  6. Indexing .mp4 52.7 MB
  6. Matrix Calculations .mp4 76.2 MB
  6. Numpy and Pandas .mp4 95.4 MB
  6. Pseudocode .mp4 66.2 MB
  6. Scientific Notation .mp4 46.1 MB
  6. Starting a Jupyter Notebook and Session .mp4 66.4 MB
  6. The find_all() Method Part 2 .mp4 82.3 MB
  6. Web Scraping with BeautifulSoup .mp4 148.9 MB
  6. while Loops .mp4 56.2 MB
  6 199.8 KB
  7. Asking for Input .mp4 67.2 MB
  7. Challenge .mp4 112.7 MB
  7 549.3 KB
  7. Closing a Jupyter Notebook Session .mp4 13.6 MB
  7. Collections Dictionaries .mp4 132.1 MB
  7. Data Exploration .mp4 133.4 MB
  7. HungryBear Production Code Part 2 .mp4 133.3 MB
  7. LaTex for Equations and Formulas .mp4 59.3 MB
  7. Slicing .mp4 59 MB
  7. The Data Science Roadmap .mp4 61.1 MB
  8 611 KB
  8. Collections Tuples, and Sets .mp4 61.6 MB
  8. Explore Visual Code for Data Science .mp4 45.9 MB
  9 646.3 KB
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  ▲ 311 total files

Description


Description

This intermediate Programming for Data Science training prepares learners to write code that makes sense of unstructured sets from multiple channels and sources and processes information you need, how you need it.

Coding and programming is fundamental to data science. If you want a career in data science, you have to plan on learning at least one or two programming languages, or else prepare yourself for a job hemmed in and restricted by whatever programs you happen to get your hands on.

When you learn programming for data science, you unlock the power of making your data do exactly what you’d like it to do for you. Without programming, your results and findings are dependent on someone else’s program and code — unlock your own future in data science by learning a programming language.

Once you’re done with this Programming for Data Science training, you’ll know how to write code that makes sense of unstructured sets from multiple channels and sources and processes information you need, how you need it.

For anyone who leads an IT team, this Data Science training can be used to onboard new data analysts, curated into individual or team training plans, or as a Data Science reference resource.
Programming for Data Science: What You Need to Know

This Programming for Data Science training has videos that cover topics including:

Writing reusable Python functions for data science
Writing Python code using object-oriented programming (OOP)
Wrangling data with Numpy and Pandas
Visualizing data with Matplotlib and Seaborn

Who Should Take Programming for Data Science Training?

This Programming for Data Science training is considered associate-level Data Science training, which means it was designed for data analysts and data scientists. This data science skills course is designed for data analysts with three to five years of experience with data science.

New or aspiring data analysts. Brand new data analysts should get started with a course like this that familiarizes them with all the programming language options that are out there. Start your career off with a primer in how analysis becomes more useful and faster with the right coding languages, and get started writing in them.

Experienced data analysts. If you’ve been working as a data analyst for several years and haven’t learned a programming language yet, this course can help you understand why it’s important and which one would be the right fit for you. Learning a coding language isn’t as daunting as you might think — try out this course and see how to incorporate programming into your data science.

Released 4/2023

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