111 Pandas courses

PYTHON BASICS: COURSE DESCRIPTION Excellent for beginners, practical, in small groups of max 4 people, 1 Day Online Instructor-led. You could contact us for your prefereed date. Session 1: Python Data Types and Variables: * Primitive types; Characters & Strings; Boolean; Working with variables and its scope; Conversion and casting types in Python. * Operators and Expressions: * Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators.. * Exercise: Calculate Movie Tickets for a Party, Are there enough seats in the cinema? Decision Making & Loops * If statement; If - else statement; If- elif - else statement; Nested if - else; * Exercise: Calculate the travel fee to deliver goods * The while, For loop * Jump statements: break, continue; * Nesting loops. * Exercise: Enter a password, if incorrect 3 times, you are blocked. Session 2: Data Structures * Lists. * Tuples. * Exercise: Hangman Game * Exercise: Get a word for the game from a Json File, store the high score in a Dictionary file Session 3: Files and exceptions * Exception Handling, Exception types; Using try and Except. * Files, streams: Open, Traverse, Read and Create Files: Csv, txt and Json Files. * API: Connecting to API’s. Session 4: OOP * Creating and using custom Functions. * Using parameters and return values. * Creating a Class; Creating an Object; Using an Object; Adding Instance variables; * Class Constructors; Parameterized Constructors. * Inheritance. Override. Session 5: Pandas Dataframe Basics * Getting data into a dataframe: Dict to Dataframe, Dataframe to Dict. Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets. * Getting information about the dataframe, * Filter, sort and query a Dataframes, Slicing Dataframes, * Duplicate values,Working with null-values, Sampling. * Exercise: Query the top 1000 grossing movies of the last century Session 6: Built in Functions: String, Math, Random * Python built-in functions: * Strings functions. Maths functions. Random Functions. * Exercise: Find information in prose, to get the sentiment of the prose. * Exercise: Get a word for the game from a txt File * Exercise: Win the lottery INCLUDED: * PCWorkshops's Python Programming Basics Certification * Course notes, exercises and code examples * Revision session after the course -------------------------------------------------------------------------------- REFUND POLICY No Refunds

This Python BootCamp is Instructor-led, Practical. In the12-week Python course, learn start to in-depth, leading to a good Python career. -------------------------------------------------------------------------------- PYTHON BOOTCAMP: This 12-week Python Boot Camp is a practical, instructor-lead program, covering Python from start to in-depth. You will be fully fluent and confident as a Python programmer. If you have more questions goto https://pcworkshopslondon.co.uk/contact.html  [http://pcworkshopslondon.co.uk/contact.html], Or contact us on training@pcworkshopslondon.co.uk [https://pcworkshopslondon.co.uk/] Customise dates, course outline, arrange installments [https://pcworkshopslondon.co.uk/contact.htm] This course will give you enough practical experience and practical projects to code, to give you full confidence to enter into the coding profession.    Duration: 3 months: * 1 Python class per week, * Plus pratical work, * Plus personal trainer-mentor for 1-1 training, * Plus e-learning materials. Final project : Practical to upload to GitHub and show-case Date and times, choose: * Fridays in London or Online , 10am - 5pm, * or Saturdays in London or Online , 12noon-6pm, * or negotiate your date Study level: Start from beginners level to in-depth, ready to work professionally. Virtual attendance:  online instructor-led  Download: Anaconda.com Pre-requisites: General computer literacy. Oracle Qualification: PCWorkshops Python Programmer Certificate Payments:  You may apply to pay in installments for this Python Training course COURSE OUTLINE Week 1 - 4: Essentials 1. Python Coding Basics 2. Object Oriented programming: Python Object Orientated programming (OOP) 3. UX Principles: UX Principles and applying it on Front-ends with TKinter 4. Specialised topics: Dates, Localization, Strings, Maths Operation , Random Number, Lambdas Week 5 - 10 : All about data 1. Python Data Structures: Lists, Tuples, Sets, Exceptions, I/O Fundamentals , Reading and Writing file 2. Database: Database principles and SQL. Database Project: Python database connections and creating a database driven project 3. Data Analytics: Numpy. Pandas for data analytics and data queries. 4. Data Analytics: Pandas data cleaning and restructuring, interacticting with Excel, Csv, Json,etc. 5. Data visualisation: MatPlotLib 6. Prediction: Machine Learning Basics Week 11 and 12: The final touch 1. Python Concurrency and Multi-threading: Threads vs. Processes, Threading Module, Threading Event, Stop a Thread, Daemon Threads, Thread-safe Queue, Thread Pools, Locks 2. Python Unit Testing 3. Optional : Replace Week 11 or 12 with Scraping using Python, ot Tkinter Front-ends INCLUDED: * PCWorkshops Python Course Certificate on completion. * Python Course notes. * Practical Course exercises, Code Examples, Online Materials. * After the course, continuous assistance with practical and preparation for exams * Max group size on this is 4. * Portfolio: Post your Python project online. * Exam preparation and interview questions MORE ABOUT THE ONLINE CLASSROOM *  Attend from your internet connection *  Instructor-led, get instant answers to your questions *  Fully interactive *  Work clearly explained with demonstrations and examples *  Practical exercises to be tried out by delegate WHAT YOU WILL GAIN: * Skills & knowledge: Python coding knowledge and understanding with good practical application   * Certification: Internal PCWorkshops Python certificate * Portfolio: You will have an online portfolio of Python projects  * Experience: Our comprehensive practical makes you job ready -------------------------------------------------------------------------------- REFUND POLICY No Refunds

This beginner's level course on Pandas acquaints you to the data analysis fundamentals using Pandas library and working with Pandas primary data structures, Series and DataFrame. Learn all about data input using indexing and filtering, data preprocessing, and handle missing values and duplicate rows to transform your data more efficiently with the help of this hands-on course.

This course offers an immersive experience in data analysis, guiding you from initial setup with Python and Pandas, through series and DataFrame manipulation, to advanced data visualization techniques. Perfect for enhancing your data handling and analysis skills.

This course will help you understand the fundamentals of data analysis with Python and Pandas library.

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

Welcome to the data manipulation in Python course. Our goal in this course is to provide you with all the tools and skills necessary to master Python, NumPy, and Pandas for data science. No previous skills or expertise are required. Only a drive to succeed!

Dive deep into the vast realm of Python data science with our meticulously crafted course: 'Python Data Science with Numpy, Pandas and Matplotlib'. Explore the intricate details of Python, setting the stage with Pandas and Numpy, before delving into the power of Python data structures. With topics ranging from Python Strings to Matplotlib Histograms, you'll gain a holistic insight, ensuring that every dataset you touch unveils its story compellingly. So, if you're keen on transmuting raw data into visual masterpieces or insights, this journey is tailor-made for you. Learning Outcomes * Grasp foundational knowledge of Python and its data structures like strings, lists, and dictionaries. * Understand the potential of NumPy, from basic array operations to handling multi-dimensional arrays. * Master the versatility of Pandas, encompassing everything from dataframe conversions to intricate operations like aggregation and binning. * Efficiently manage, manipulate, and transform data using Pandas' diverse functionalities. * Create visually striking and informative graphs using the power of Matplotlib. WHY BUY THIS PYTHON DATA SCIENCE WITH NUMPY, PANDAS AND MATPLOTLIB COURSE? 1. Unlimited access to the course for forever 2. Digital Certificate, Transcript, student ID all included in the price 3. Absolutely no hidden fees 4. Directly receive CPD accredited qualifications after course completion 5. Receive one to one assistance on every weekday from professionals 6. Immediately receive the PDF certificate after passing 7. Receive the original copies of your certificate and transcript on the next working day 8. Easily learn the skills and knowledge from the comfort of your home CERTIFICATION After studying the course materials of the Python Data Science with Numpy, Pandas and Matplotlib there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. WHO IS THIS PYTHON DATA SCIENCE WITH NUMPY, PANDAS AND MATPLOTLIB COURSE FOR? * Beginners eager to jumpstart their journey in Python data science. * Analysts looking to enhance their data manipulation skills using Python. * Statisticians keen on expanding their toolset with Python-based libraries. * Data enthusiasts desiring a deep dive into Python's data libraries and structures. * Professionals aiming to upgrade their data visualisation techniques. PREREQUISITES This Python Data Science with Numpy, Pandas and Matplotlib does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Python Data Science with Numpy, Pandas and Matplotlib was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. CAREER PATH * Data Scientist: £40,000 - £80,000 * Python Developer: £35,000 - £70,000 * Data Analyst: £30,000 - £55,000 * Business Intelligence Analyst: £32,000 - £60,000 * Research Analyst: £28,000 - £52,000 * Data Visualization Engineer: £33,000 - £65,000 COURSE CURRICULUM Course Introduction and Table of Contents Course Introduction and Table of Contents 00:09:00 Introduction to Python, Pandas and Numpy Introduction to Python, Pandas and Numpy 00:07:00 System and Environment Setup System and Environment Setup 00:08:00 Python Strings Python Strings - Part 1 00:11:00 Python Strings - Part 2 00:09:00 Python Numbers and Operators Python Numbers and Operators - Part 1 00:06:00 Python Numbers and Operators - Part 2 00:07:00 Python Lists Python Lists - Part 1 00:05:00 Python Lists - Part 2 00:06:00 Python Lists - Part 3 00:05:00 Python Lists - Part 4 00:07:00 Python Lists - Part 5 00:07:00 Tuples in Python Tuples in Python 00:06:00 Sets in Python Sets in Python - Part 1 00:05:00 Sets in Python - Part 2 00:04:00 Python Dictionary Python Dictionary - Part 1 00:07:00 Python Dictionary - Part 2 00:07:00 NumPy Library - Introduction NumPy Library Intro - Part 1 00:05:00 NumPy Library Intro - Part 2 00:05:00 NumPy Library Intro - Part 3 00:06:00 NumPy Array Operations and Indexing NumPy Array Operations and Indexing - Part 1 00:04:00 NumPy Array Operations and Indexing - Part 2 00:06:00 NumPy Multi-Dimensional Arrays NumPy Multi-Dimensional Arrays - Part 1 00:07:00 NumPy Multi-Dimensional Arrays - Part 2 00:06:00 NumPy Multi-Dimensional Arrays - Part 3 00:05:00 Introduction to Pandas Series Introduction to Pandas Series 00:08:00 Introduction to Pandas Dataframes Introduction to Pandas Dataframes 00:07:00 Pandas Dataframe conversion and drop Pandas Dataframe conversion and drop - Part 1 00:06:00 Pandas Dataframe conversion and drop - Part 2 00:06:00 Pandas Dataframe conversion and drop - Part 3 00:07:00 Pandas Dataframe summary and selection Pandas Dataframe summary and selection - Part 1 00:06:00 Pandas Dataframe summary and selection - Part 2 00:06:00 Pandas Dataframe summary and selection - Part 3 00:07:00 Pandas Missing Data Management and Sorting Pandas Missing Data Management and Sorting - Part 1 00:07:00 Pandas Missing Data Management and Sorting - Part 2 00:07:00 Pandas Hierarchical-Multi Indexing Pandas Hierarchical-Multi Indexing 00:06:00 Pandas CSV File Read Write Pandas CSV File Read Write - Part 1 00:05:00 Pandas CSV File Read Write - Part 2 00:07:00 Pandas JSON File Read Write Pandas JSON File Read Write Operations 00:07:00 Pandas Concatenation Merging and Joining Pandas Concatenation Merging and Joining - Part 1 00:05:00 Pandas Concatenation Merging and Joining - Part 2 00:04:00 Pandas Concatenation Merging and Joining - Part 3 00:04:00 Pandas Stacking and Pivoting Pandas Stacking and Pivoting - Part 1 00:06:00 Pandas Stacking and Pivoting - Part 2 00:05:00 Pandas Duplicate Data Management Pandas Duplicate Data Management 00:07:00 Pandas Mapping Pandas Mapping 00:04:00 Pandas Grouping Pandas Groupby 00:06:00 Pandas Aggregation Pandas Aggregation 00:09:00 Pandas Binning or Bucketing Pandas Binning or Bucketing 00:08:00 Pandas Re-index and Rename Pandas Re-index and Rename - Part 1 00:04:00 Pandas Re-index and Rename - Part 2 00:05:00 Pandas Replace Values Pandas Replace Values 00:05:00 Pandas Dataframe Metrics Pandas Dataframe Metrics 00:07:00 Pandas Random Permutation Pandas Random Permutation 00:08:00 Pandas Excel sheet Import Pandas Excel sheet Import 00:07:00 Pandas Condition Selection and Lambda Function Pandas Condition Selection and Lambda Function - Part 1 00:05:00 Pandas Condition Selection and Lambda Function - Part 2 00:05:00 Pandas Ranks Min Max Pandas Ranks Min Max 00:06:00 Pandas Cross Tabulation Pandas Cross Tabulation 00:07:00 Matplotlib Graphs and plots Graphs and plots using Matplotlib - Part 1 00:06:00 Graphs and plots using Matplotlib - Part 2 00:02:00 Matplotlib Histograms Matplotlib Histograms 00:03:00 Resource File Resource File - Python Data Science with Numpy, Pandas and Matplotlib 00:00:00

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. INTRODUCTION TO DATA ANALYSIS * Fundamentals of data analysis * Statistical foundations * Setting up a virtual environment WORKING WITH PANDAS DATAFRAMES * Pandas data structures * Bringing data into a pandas DataFrame * Inspecting a DataFrame object * Grabbing subsets of the data * Adding and removing data DATA WRANGLING WITH PANDAS * What is data wrangling? * Collecting temperature data * Cleaning up the data * Restructuring the data * Handling duplicate, missing, or invalid data AGGREGATING PANDAS DATAFRAMES * Database-style operations on DataFrames * DataFrame operations * Aggregations with pandas and numpy * Time series VISUALIZING DATA WITH PANDAS AND MATPLOTLIB * An introduction to matplotlib * Plotting with pandas * The pandas.plotting subpackage PLOTTING WITH SEABORN AND CUSTOMIZATION TECHNIQUES * Utilizing seaborn for advanced plotting * Formatting * Customizing visualizations FINANCIAL ANALYSIS - BITCOIN AND THE STOCK MARKET * Building a Python package * Data extraction with pandas * Exploratory data analysis * Technical analysis of financial instruments * Modeling performance RULE-BASED ANOMALY DETECTION * Simulating login attempts * Exploratory data analysis * Rule-based anomaly detection GETTING STARTED WITH MACHINE LEARNING IN PYTHON * Learning the lingo * Exploratory data analysis * Preprocessing data * Clustering * Regression * Classification MAKING BETTER PREDICTIONS - OPTIMIZING MODELS * Hyperparameter tuning with grid search * Feature engineering * Ensemble methods * Inspecting classification prediction confidence * Addressing class imbalance * Regularization MACHINE LEARNING ANOMALY DETECTION * Exploring the data * Unsupervised methods * Supervised methods * Online learning THE ROAD AHEAD * Data resources * Practicing working with data * Python practice

This course first equips you with the fundamentals of Python and then progresses to teach you how to use various libraries such as NumPy, Pandas, Seaborn, Bokeh, and so on. This course contains several mini projects so that, by the end of this course, you will be equipped with the essential tools you need to become a visualization expert.