124 Pandas courses delivered Online

The course helps you learn Snowflake from scratch and explore a few of its important features. You will build automated pipelines with Snowflake and use the AWS cloud with Snowflake as a data warehouse. You will also explore Snowpark to be worked on the data pipelines.

You will learn how to become a Python developer through this course.

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Welcome to this dual-phase course. In the first segment, we delve into neural networks and deep learning. In the second, ascend to mastering Generative Adversarial Networks (GANs). No programming experience required. Begin with the fundamentals and progress to an advanced level.

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Learn how to create an automated trading bot using Python with this comprehensive course. Master Python basics, understand trading fundamentals, build and integrate the bot with a broker API, and run it effectively. Learning Outcomes: Gain proficiency in Python programming for trading purposes. Understand the fundamental concepts of trading and market dynamics. Build a structured trading bot using Python and Github version control. Integrate the bot with a broker API for real-time trading functionality. Successfully run and manage the automated trading bot for efficient execution. Why buy this Making Automated Trading Bot Using Python? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Making Automated Trading Bot Using Python 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 course for? This Making Automated Trading Bot Using Python course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Making Automated Trading Bot Using Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Making Automated Trading Bot Using Python 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 As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Making Automated Trading Bot Using Python is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Introduction About the course structure 00:05:00 Why working is important? 00:04:00 The free and perfect tools 00:07:00 Our editor: Atom 00:04:00 Version control: Github 00:07:00 Python download (Mac) 00:05:00 Python download (Windows) 00:02:00 Section 02: Python Basics for Trading Introduction 00:03:00 Python Libraries 00:05:00 Iterators: for 00:08:00 Iterators: while 00:08:00 Conditionals: if & else 00:10:00 Logic gates: and & or 00:09:00 Error handling: try & except 00:09:00 Functions and calls to libraries 00:13:00 Objects and classes (1) 00:10:00 Objects and classes (2) 00:07:00 Debugging the code 00:12:00 Closing and wrap up 00:01:00 Section 03: Trading Basics Introduction 00:03:00 Fundamental vs Technical Analysis 00:04:00 Stocks vs CFDs 00:05:00 Long and Short positions 00:04:00 Takeprofit and Stoploss 00:03:00 Setting a real Stoploss 00:08:00 Limit and Market orders 00:10:00 Don't forget the spread 00:04:00 Stock data visualisation: candles 00:08:00 Technical Indicators: about 00:05:00 Exponential Moving Average 00:08:00 EMA use and interpretation 00:06:00 Relative Strength Index 00:07:00 Stochastic Oscillator 00:09:00 Closing and wrap up 00:01:00 Section 04: Bot Code General Structure Introduction 00:02:00 Overview 00:08:00 The Entry Strategy 00:10:00 About Tradingview 00:12:00 When to enter (1) 00:08:00 When to enter (2) 00:08:00 Open and hold a position 00:12:00 Closing a position 00:08:00 Review (1) 00:06:00 Review (2) 00:13:00 Closing 00:02:00 Section 05: Github Basics Introduction 00:04:00 Download and install Github 00:01:00 Create a repo 00:10:00 Working with branches 00:13:00 Section 06: Building Your Bot Introduction 00:05:00 Create the first bot file 00:07:00 Building the bot scheme 00:08:00 Complete your code scheme (1) 00:11:00 Complete your code scheme (2) 00:11:00 Complete your code scheme (3) 00:18:00 A logger to remember (1) 00:14:00 A logger to remember (2) 00:14:00 Organising your code 00:07:00 Main function: run bot 00:23:00 Link the bot and the library 00:08:00 Traderlib control functions (1) 00:12:00 Traderlib control functions (2) 00:13:00 Check if tradable function 00:06:00 Set stoploss function 00:10:00 Set takeprofit function 00:04:00 Load historical data function 00:01:00 Get open positions function 00:04:00 Submit and cancel order functions 00:04:00 Check positions function 00:09:00 The Tulipy libraries 00:07:00 Importing all the libraries 00:03:00 First filter: get general trend 00:19:00 Second filter: get instant trend 00:14:00 Third filter: RSI 00:08:00 Fourth filter: Stochastic Oscillator 00:14:00 Enter position (1) 00:13:00 Enter position (2) 00:11:00 Enter position (3) 00:15:00 Enter position (4) 00:08:00 Last check before opening 00:13:00 Exit position and get out 00:10:00 Linking everything (1) 00:12:00 Linking everything (2) 00:12:00 Linking everything (3) 00:15:00 Fixing a mistake: going Short 00:05:00 Handling all your variables 00:18:00 Closing and wrap up 00:01:00 Run function scheme clarification and rebuild 00:13:00 Section 07: Integrating the Broker API Introduction 00:03:00 The Alpaca Python API Wrapper 00:07:00 Initialising the REST API 00:09:00 Running the program (crash!) 00:06:00 Integration with check account (1) 00:08:00 Integration with check account (2) 00:05:00 Clean open orders function 00:10:00 Importing the trading library 00:04:00 Running the main 00:05:00 Check position function 00:09:00 Check if asset exists function 00:08:00 Fetching barset data (theory) 00:07:00 Fetching barset data (practice) 00:12:00 Updating the code for the Alpaca API V2 (explanation) 00:03:00 Updating the code for the Alpaca API V2 (implementation) 00:08:00 Organizing data with Pandas 00:06:00 Get general trend function (1) 00:08:00 Reframing the timeframe with Pandas 00:23:00 Get general trend function (2) 00:13:00 Get instant trend function 00:08:00 Get RSI function 00:06:00 Get Stochastic function 00:10:00 Get current price function 00:05:00 Finishing get shares amount 00:09:00 Opening a position (1) 00:12:00 Opening a position (2) 00:09:00 Check the open position 00:07:00 Cancelling the order (1) 00:20:00 Cancelling the order (2) 00:08:00 Making sure we cancelled 00:03:00 Get average entry price function 00:10:00 Fixing bugs when getting price 00:18:00 Check Stochastic crossing 00:02:00 Holding an open position 00:11:00 Submitting the exit order 00:08:00 Closing position and out (1) 00:08:00 Closing position and out (2) 00:10:00 Closing and wrap up 00:01:00 Section 08: Running the Trading Bot Introduction 00:03:00 Filtering asset by price and volume 00:07:00 Get the bot ready to trade 00:04:00 Running the Trading Bot with AAPL 00:09:00 A real open position 00:08:00 Debugging and bug fixing 00:12:00 Fixing one (last) bug 00:02:00 Running the bot with TSLA 00:10:00 Discussing EMA implementations 00:12:00 Closing and wrap up 00:02:00

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. An Overview of Python Why Python? Python in the Shell Python in Web Notebooks (iPython, Jupyter, Zeppelin) Demo: Python, Notebooks, and Data Science Getting Started Using variables Builtin functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control White space Conditional expressions Relational and Boolean operators While loops Alternate loop exits Sequences, Arrays, Dictionaries and Sets About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Generator Expressions Nested sequences Working with Dictionaries Working with Sets Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Functions Defining functions Parameters Global and local scope Nested functions Returning values Essential Demos Sorting Exceptions Importing Modules Classes Regular Expressions The standard library Math functions The string module Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Python and Data Science Data Science Essentials Pandas Overview NumPy Overview SciKit Overview MatPlotLib Overview Working with Python in Data Science Additional course details: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Python for Data Science: Hands-on Technical Overview (TTPS4873) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.

This comprehensive course will guide you to use the power of Python to evaluate recommender system datasets based on user ratings, user choices, music genres, categories of movies, and their years of release with a practical approach to build content-based and collaborative filtering techniques for recommender systems with hands-on experience.