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Python for Data Analysts & Quants training course

Dive into the world of Python with our specialised training course tailored for data analysts and Quants. Unlock the power of Python programming to enhance your analytical skills and cater to the unique needs of the financial industry. Elevate your proficiency in Python, data analysis, and quantitative techniques with our expert-led training course. Enroll now to master Python and excel in the realms of data analytics and quantitative finance. Learn to use Python, Pandas, and statistical computing libraries to analyse & visualise data and to gather actionable insights.

JBI training course London UK

"I hadn't integrated Pandas with Python before joining my company. So it's very useful to consolidate my understanding of such skills via this course. The Jupyter notebooks provided will be a valuable resource for revising the materials and are really well laid out. Professional standards." 

JL, Data Analyst, Python for Data Analytics, March 2021

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26/03/24 - 3 days
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JBI training course London UK

  • Learn core concepts of the Python environment, language & data science.
  • Use Python to get your Excel and SQL data in shape, and take advantage of its features and techniques to gain actionable Insights
  • Explore Python data science tools such as NumPy and Pandas  
  • Explore the opportunity to apply the proposed concepts on real data science applications
  • Acquire knowledge on how to access and clean/prepare data 
  • Use data analysis to perform the computation of summary information and basic statistics
  • Use effective data visualisation techniques to help you with complex data structures

Python for Data Analysts & Quants training course:

 

  • Master Data Analysis with Python: Learn the fundamentals of data analysis using Python, gaining proficiency in data manipulation, cleaning, and visualization with popular libraries like Pandas and Matplotlib.
  • Quantitative Finance Techniques: Dive into quantitative finance methodologies, exploring statistical models, time series analysis, and risk management strategies essential for financial data interpretation.
  • Real-world Applications: Apply Python to solve real-world financial problems and make data-driven decisions, with hands-on projects covering portfolio optimization, asset pricing, and risk assessment.

 

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook

Python core concepts

• Built-in data types in Python
• Working with strings, numbers, lists, tuples and dictionaries
• Control flow statements
• Conditional execution with if statements
• Conditional loops with where statements
• Looping over a sequence with for statements
• Defining and using custom functions
• Working with dates and times
• Accessing data on file (CSV, JSON, ...)

Python Data Science libraries

• Working with data in pandas

  • Working with table-like data in pandas
  • Creating Series and DataFrame objects
  • Loading data from file into DataFrame objects
  • Adding, removing and updating databases

Retrieving data in pandas

  • Querying data to extract specific rows and columns
  • Filtering data on specific conditions
  • Understanding pandas indexing

Data manipulation in pandas

  • Data transformation
  • Applying functions to transform individual values
  • Applying functions to aggregate values by rows and columns
  • Data Transformation for Python for Data Analysts & Quants. 

 

Missing data in pandas

  • Identifying missing data points
  • Filtering out missing data
  • Filling missing data with given values, interpolation and other filling strategies

Data Analysis in pandas

  • Extracting summary statistics over DataFrame objects
  • Performing data aggregation queries (groupby() method)
  • Aggregating multiple columns in the same query
  • Exploratory analysis of new datasets

Data Visualisation in pandas

  • Plotting data from a Series or DataFrame object
  • Bar plots, line plots, scatter plots, histograms and other common charts
  • Basic customisation of charts

Working with multiple tables

  • Concatenation of multiple tables based on structure/schema
  • Join/merge operations with DataFrame objects based on values
  • Reindexing operations, dealing with duplicate labels in the index
  • Dealing with duplicate records
  • Renaming columns

 Time Series with pandas

  • Working with date and time data types in pandas
  • Creating ranges of date/time data points
  • Indexing by time
  • Resampling: data aggregation over time
  • Moving window operations, e.g. moving average
  • Defining custom calendars, custom business days, custom holidays

 

 

Working with text data in DataFrames

  • Using the str attribute in pandas objects
  • String manipulation functions in pandas
  • Filtering data with string pattern matching

SQL databases

  • Connecting to SQL databases with SQLAlchemy
  • Loading data from SQL to pandas
  • Sending SQL queries to a database and retrieving the results in Python and pandas

NumPy

  • Working with multi-dimensional arrays with NumPy
  • Arithmetic operations with NumPy arrays
  • Vectorised operations with NumPy arrays
  • Stats and linear algebra with NumPy
  • Slicing and indexing NumPy arrays

Data Visualisation with matplotlib and plotly

  • Overview on the basic types of charts available with the Python libraries
  • Bar plots, line plots, histograms, scatter plots, pie charts
  • Customising the layout and format of a chart
  • Examples of static visualisation with matplotlib
  • Examples of interactive visualisation with plotly
JBI training course London UK

Unlock the power of Python for data-driven financial decision-making! This comprehensive ( Python for Data Analysts & Quants training course ) is tailored for aspiring professionals, including:

Finance and Investment Analysts:

Dive into Python to enhance your analytical skills, gain insights from financial data, and optimize investment strategies for better returns.

Data Scientists and Analysts:

Elevate your data analysis capabilities by specializing in quantitative finance. Learn to apply Python for statistical modeling, machine learning, and interactive visualization in the finance domain.

Financial Planners and Advisors:

Stay ahead in the competitive finance landscape by mastering Python tools for risk assessment, portfolio optimization, and algorithmic trading, ensuring better-informed financial planning.

Graduates and Students in Finance:

Kickstart your career with a solid foundation in Python for data analysis and quantitative finance. Acquire skills that are in high demand in the financial industry.

Business Professionals in Finance:

Unleash the potential of Python to analyze market trends, assess risks, and make informed business decisions. This course provides practical skills applicable to a wide range of financial roles.

Risk Managers and Compliance Professionals:

Strengthen your risk management skills using Python, exploring techniques for identifying and mitigating financial risks in a dynamic market environment.

Whether you are a finance professional aiming to upskill, a data enthusiast venturing into the finance sector, or a student eager to specialize, this course equips you with the Python proficiency needed for success in data analysis and quantitative finance. Enroll now to future-proof your career!

Quants, Data Scientists, Data Analysts, Financial Analysts, Researchers, Business Intelligence experts and Developers who are new to Python.

Software developers who are new to Python and Data Science or want to know more about Python tools for Data Analysis.


5 star

4.8 out of 5 average

"I hadn't integrated Pandas with Python before joining my company. So it's very useful to consolidate my understanding of such skills via this course. The Jupyter notebooks provided will be a valuable resource for revising the materials and are really well laid out. Professional standards." 

JL, Data Analyst, Python for Data Analytics, March 2021

JBI training course London UK

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                                      Python for Data Analysts & Quants 

                                       training course

 

  • Algorithmic Trading Strategies: Develop and implement algorithmic trading strategies using Python, covering topics such as moving averages, momentum trading, and statistical arbitrage.

  • Interactive Data Visualization: Harness the power of interactive data visualization tools like Plotly and Seaborn to communicate complex financial data insights effectively.

  • Machine Learning for Finance: Explore machine learning applications in finance, from predictive modeling for stock prices to sentiment analysis for trading signals, utilizing scikit-learn and TensorFlow.

  • Risk Management and Portfolio Optimization: Understand the principles of risk management and portfolio optimization, applying mathematical models to create well-balanced investment portfolios.

  • Time Series Analysis: Master time series analysis techniques for financial data, including autoregressive models, moving averages, and volatility forecasting, enhancing your ability to predict market trends.

 

What is the balance between theoretical instruction and functional application in the curriculum of the Python training course?

The Python data science courses are meticulously structured to address both theoretical knowledge and practical skills required for effective programming. It provides a strong theoretical foundation of Python, explaining core concepts such as data types, control structures, and object-oriented programming methodologies. This theoretical scaffolding ensures subjects understand the 'why' behind Python’s functionalities.

In addition to theoretical understanding, the Python data science courses place professional significant emphasis on equipping students with practical skills. Numerous hands-on coding exercises and real-world projects are interwoven throughout the data science courses, allowing students to apply learned concepts directly. From creating simple scripts to more complex applications, these practical components ensure students not only understand Python theoretically but also gain proficiency in applying Python to solve problems.

Hence, the Python data analytics courses focus on both theoretical understanding and practical skills, recognising the importance of both for comprehensive learning of Python. Its balanced approach facilitates a deep, versatile understanding of Python, preparing students adequately for varied programming scenarios.

Why should Quants learn Python programming skills?

Every individual venturing into the increasingly complex financial world recognises the value of quantitative analysis, or "quant." This data-analytical function is a game-changer in investment decision-making, risk management, pricing securities, and more. But to truly harness the power of quant, a modern tool is needed. Here comes Python, a generalised programming language that has steadily become a favorite among Quants. This article explores the reasons why Python training is invaluable for Quants.

Python's Versatility and Efficiency:

"How to enhance the diversity and efficiency of my data processing?" Ask this question and the answer points towards Python. The language's design allows it to manage different data formats, handle large data sets, and execute complex mathematical computations - all vital for quantitative analysis. The simplicity of Python code further enhances efficiency, which is critical for high-speed, real-time processes integral to finance.

Python Libraries: A Treasure Trove for Quants:

Python’s extensive libraries like NumPy, SciPy, Pandas, and Matplotlib come up when people ask, "What tools can streamline my quantitative finance tasks?". These libraries offer pre-written functions, saving time and effort in creating complex algorithms from scratch. Their applications range from statistical analysis, numerical computations, data manipulation to visualization - all of which are central to any Quant’s job.

Machine Learning (ML) and Artificial Intelligence (AI) Compatibility:

For Quants inquiring, "How can I incorporate ML & AI into my analysis?", Python provides the answer. It is one of the most used languages in AI and ML, with libraries like Scikit-learn, TensorFlow, and Keras. These technologies, combined with quantitative analysis, propel trading strategies, risk modeling, and market predictions to a whole new level of accuracy.

Python's Growing Popularity and Community:

Python has a rapidly expanding user community that helps improve and expand the language continuously. The broad ecosystem is always ready to solve "Who can answer my Python-related queries?". New tools, tutorials, and support from experienced Python users keep improving, which helps Quants to stay updated and solve coding problems efficiently.

Python Enhances Employability:

For Quants wondering, "How can I increase my value in the job market?", becoming proficient in Python is a sensible move. In an industry where the landscape is quickly shifting towards algorithmic-based strategies and automated risk analytics, a (Quant) well-versed in (Python) is a desirable asset to employers.

In conclusion, Python's scalability, versatility, and powerful features make it a potent tool in a Quant’s arsenal. Offering solutions to varied Quants’ queries, Python has proved its worth in the Quant world. By undertaking Python training, Quants open a new vista of opportunities, enhancing their capability to navigate the ocean of numbers, patterns, and trends that is modern finance. Investing time in mastering Python today is synonymous with investing in a flourishing Quant career tomorrow.

 

What makes Python a versatile programming language for a data analyst?

In the vibrant landscape of programming languages, Python emerges as a versatile frontrunner, with its radiance reaching far beyond the coding realm. It meanders through various domains like a mighty river, extending from web development and data analytics to AI and machine learning. What emboldens its versatility? The answer lies sequestered in its elegant simplicity, unrivaled flexibility, and extensive array of libraries.

Our Python data analytics course embraces beginners with its simplicity. It boasts the syntax of a high-level language, which gives it an excellent readability quotient and makes it accessible to programming novices. A script written in Python is more like reading an engrossing novel rather than deciphering cryptographic code, and it’s this user-friendly design that contributes to its soaring popularity amongst programmers. In Python's world, less is more; its philosophy encourages concise yet effective code.

The versatility of Python is also fortified by the formidable battalion of libraries it houses. The language is like a treasure chest filled with the exuberant gemstones of libraries, ready to be harnessed at a moment's notice. With NumPy and pandas for data manipulation, Matplotlib for data visualization, or TensorFlow and PyTorch for machine learning, Python's library collection is a generosity most other programming languages could only dream of offering.

And let's not forget the awe-inspiring flexibility. (Python) is a cross-platform language, meaning it soothes programmers' lives by being executable on various operating systems without hampering the performance. Given its interpretive nature, Python can run the code immediately after it's written, significantly reducing the edit-compile-run time and making it a go-to for rapid prototyping.

Conclusively, Python is the Swiss Army Knife of programming languages, playing multiple roles with ease and finesse. Its simplicity, flexibility, and extensive libraries foster an environment where creativity thrives, making it the versatile hero in the narrative of programming languages. Its notability secures its position in a league of its own, making it a protagonist that’s here to stay in the unfolding saga of computational technology.

What methods of study are available for Data analysis courses? ( PYTHON ) 

Our data analyst course offers various flexible methods of study to accommodate different learning styles:

  • Live online - Join scheduled virtual classes led by an instructor in real-time. Engage with peers.
  • Blended - Combine self-study materials with live online or in-person workshops. Get the best of both worlds.
  • In-person - Attend classes at a local training centre with hands-on instruction and equipment. Ideal for immersive practical focus.
  • Customised delivery - Mix and match components to create a personalised curriculum catering to your needs.
  • Team-based - Work through the program in a cohort for collaboration and project work.
  • Accelerated formats - Intensive boot camps and workshops to rapidly build skills.

The most effective programs use a variety of approaches like lectures, interactive labs, case studies, assessments, and group work. Look for courses providing engaging instruction, peer collaboration, and real-world applications.

Deciphering Python Data Analysis Courses: Ten Key Questions Managers Ask When Guiding Their Team's Development

 

What key topics should managers look for in Python data analysis courses for their data analysts?

Managers looking for Python data analysis courses for their team should focus on the following key topics:

Data Wrangling and Cleaning

  • Importing data from different sources (SQL, CSV, JSON, etc.)
  • Handling missing data
  • Identifying and removing outliers
  • Data transformations - changing data types, encoding, etc.

Exploratory Data Analysis

  • Summary statistics and visualisations
  • Histogram, scatter plot, box plot
  • Identifying trends and patterns in data

Model Development

  • Developing and training models like linear regression, random forest, SVM
  • Evaluating model performance - R^2, RMSE, classification metrics
  • Tuning models for improved performance
  • Preventing overfitting

Productionisation

  • Building data pipelines - ETL process
  • Automating analysis through scripts/notebooks
  • Deploying models to production
  • Monitoring models post-deployment

Python Programming

  • Numpy, Pandas, Matplotlib, Seaborn
  • Scikit-learn for machine learning
  • Jupyter notebooks
  • Python best practices - modularity, documentation

Statistics and Math Fundamentals

  • Probability distributions
  • Statistical testing hypothesis
  • Correlation vs causation
  • Business metrics interpretation

The curriculum should focus on real-world case studies and hands-on exercises. The opportunity to work on team projects analysing datasets can cement these skills. Assessing proficiency through quizzes and culminating project is important.

What are the prerequisites that managers should be aware of when selecting Python data analysis data science courses for their team?

When selecting a data analysis course, managers should evaluate the trainer and training company in addition to the course content and prerequisites.

Trainer Experience

  • Years of industry and training experience
  • Domain expertise - finance, healthcare, etc.
  • Real-world analytical project examples
  • Strong communication and teaching skills

Past Clients and Reviews

  • Reputable companies as past clients
  • Positive reviews
  • Testimonials from past trainees

Course Customisation

  • Flexibility to tailor curriculum to your needs
  • Options for different experience levels
  • Custom case studies and datasets

Hands-on Focus

  • Coding exercises and projects with feedback
  • Small class sizes for individual attention
  • Access to teaching assistants

Post-training Support

  • Help desk for troubleshooting
  • Community forums to collaborate
  • Resources for continued learning

Conducting technical screening assessments, having a call with the trainer, and requesting client references can provide additional insights beyond marketing materials. Prioritising instructors with proven track records of success can increase the training ROI.

Is this "Python data analysis course" suitable for the current skill level of my team? Does it provide comprehensive coverage of key subjects from basics to advanced topics if necessary?

To evaluate if a Python data analysis course suits your team's skills and provides comprehensive coverage, consider:

Course Level

  • Are there options for beginner, intermediate, advanced?
  • Is it self-paced or do you select the level?
  • Will advanced members be engaged and challenged?

Topics Covered

  • Look at the curriculum - does it cover:
    • Python programming, libraries like Pandas
    • Statistics and math fundamentals
    • Data wrangling and cleaning hands-on
    • Exploratory data analysis
    • Model building and evaluation
    • Deployment and monitoring of models
  • Any critical topics missing for your needs?

Hands-on Focus

  • Includes practical coding assignments?
  • Real-world case studies and sample datasets?
  • Opportunities to work on team projects?

Teaching Style

  • Interactive with ability to ask questions?
  • Combination of videos, readings, live sessions?
  • Focus on both coding syntax and conceptual topics?

Assessments

  • Quizzes and exercises to reinforce concepts?
  • Final project to evaluate proficiency?
  • Certification of completion?

Review the syllabus in detail and sign up for an introductory session. Have your team complete a diagnostic assessment to gauge the current level. Discuss specific skill goals with the instructor to ensure a tailored experience.

How is the Python training course structured? Is it engaging and designed to promote knowledge retention among learners?

To evaluate if a Python data analyst training structure will effectively engage learners and promote retention, consider:

Format

  • Self-paced, live-online, or in-person? Choose based on learning style.
  • Interactive sessions or mostly videos? Mix helps sustain energy.

Schedule

  • Cohort-based, flexible, or self-paced?
  • Reasonable session length - 3-4 hours max.
  • Allow time to absorb concepts between sessions.

Teaching Methods

  • Balanced lectures, hands-on practice, discussions?
  • Opportunities to get stuck and unstuck?
  • Real-world case studies and project work?

Community

  • Collaboration with other learners?
  • Meetups and study groups?
  • Alumni channels for networking?

Assessments

  • Well-designed quizzes reinforce key concepts.
  • Low-stakes assignments provide practice.
  • Final project measures proficiency.

Post-Course

  • Ongoing access to course materials and community.
  • Resources for continued learning and growth.

A rewarding learning experience sticks. Try a sample lesson before committing. Get feedback from past trainees. Ensure ongoing access to materials, coaches, and a community forum.

Can the training course be tailored or customised according to the specific needs and use cases of our business?

Absolutely, we understand each organisation has unique needs and priorities. We work closely with clients to fully customise our Python data analysis curriculum and training approach.

Here are some examples of how we tailor to your business:

Datasets and Case Studies

  • Provide your real data for hands-on practice
  • Build case studies relevant to your industry and use cases
  • Custom visualisation dashboards

Domain Knowledge

  • Incorporate terminology, metrics, and processes specific to your work
  • Ensure conceptual frameworks match your business context

Skills Focus

  • Emphasise advanced skills like NLP or computer vision if needed
  • Introductory coverage if learners are new to Python
  • Tailor to team's learning objectives

Deployment and Infrastructure

  • Connect to your databases, BI tools, etc.
  • Deploy models on your cloud or on-premise

Final Project

  • Learners work on an analysis project for your business
  • Present insights and recommendations

We assign a dedicated account manager to understand your needs. Our trainers have experience adapting materials across industries. Please reach out to schedule a consultation so we can develop a customised learning plan for your team.

Does the data analyst course include collaborative or team-based learning assignments to enhance shared understanding and team building?

Yes, we recognise the importance of collaborative learning to promote stronger shared knowledge and team dynamics. Our Python data analysis course includes several team-based components:

  • Group Projects - Learners work in small teams on an end-to-end analysis project using real datasets. Teams present findings.
  • Peer Code Reviews - Participants review each other's code to improve coding best practices.
  • Study Groups - We facilitate study groups for learners to discuss concepts, work on exercises together.
  • Discussion Forums - Learners can share insights, post questions, and collaborate online.
  • Pair Programming - Participants team up remotely to tackle coding assignments together.
  • Team Exercises - Short team-based activities reinforce key concepts.
  • Team Assessments - Some quizzes and exercises are taken as a team.

Working together gives exposure to collaborative data science workflows. Teams can leverage members with different strengths. Social learning enhances motivation, engagement, and connection to the material.

Let us know if you have ideas for specific team projects or activities that would benefit your learners. We can incorporate into the curriculum.

Is the provider reputable with positive reviews? Can they provide case studies or testimonials from similar industries or companies?

Absolutely. We have an excellent reputation and consistently receive positive reviews for our specialized data science training programs.

Since starting in 1995, we have worked with many organisations globally, including:

  • BBC
  • Sony
  • Lloyds Bank
  • UK Government
  • NHS
  • Top Universities

Our trainers have decades of industry and instructional experience across domains like finance, healthcare, retail, and more.

Learners highlight our expert instructors, practical curriculum, and career impact.

We can connect you directly with past trainees from similar roles and industries for candid feedback. Our case studies showcase successful analytics initiatives and ROI achieved by clients through our training programs.

Please reach out to request testimonials, and client examples, or to discuss a custom engagement tailored to your needs. We're confident we can deliver a high-value learning experience for your team.
 

Is the data science course flexible to work around our team's schedules?

We are very flexible and can adapt the data science course logistics to accommodate your team's schedules and availability.

Rather than forcing employees into a rigid program, we take the time to understand your business needs and constraints. We then structure the delivery to work within those, while still upholding training quality and effectiveness.

The goal is to make our Python data analysis data science courses training fit seamlessly into your existing workflows, with minimal disruption. Employees can learn at times most convenient for them and your business. Please share your ideal schedules and constraints so we can design an optimal learning plan. We're committed to delivery flexibility and your team's success.

How will the effectiveness of the training be evaluated? Is there post-training support or resources for continual learning available?

We utilise various methods to evaluate training effectiveness and provide ongoing support for our courses:

Effectiveness Evaluation

  • Pre-training skills assessment to benchmark progress
  • Post-training knowledge tests to measure proficiency gain
  • End-of-course feedback surveys
  • Manager debriefs on team application of skills
  • Tracking career progress like promotions afterwards

Post-Training Support

  • Hotline and email helpdesk for troubleshooting
  • Knowledge base with FAQs and how-to guides
  • Community forums to discuss challenges
  • Monthly online seminars to reinforce concepts
  • Refresher classes and mentorship programs
  • Resources for self-guided continued learning

The goal is to not only equip your team with in-demand data skills through the training but set them up for continued success afterward. We're invested in their long-term growth. Post-training check-ins also help us improve course outcomes over time.

Considering our business objectives and the needs of our team, does the cost of the course provide a good return on investment?

When evaluating the ROI of training data science courses, consider both quantitative and qualitative impacts:

Quantitative ROI

  • Increased productivity and analysis throughput
  • Time and costs savings from automation
  • Revenue growth from data-driven decisions
  • Calculate using impact on key business metrics

Qualitative ROI

  • Improved data literacy and capabilities
  • Increasedemployee satisfaction and retention
  • More informed strategic planning
  • Faster adoption of new technologies
  • Data culture and team morale boost

Discuss specific KPIs we can track before and after training to quantify the impact. Conduct skills assessments to highlight individual growth.

While hard to fully calculate, enhanced data skills can unlock significant long-term value. Our courses are designed to rapidly boost proficiency worth well beyond program costs. An upfront investment often results in sustained gains.

Let's talk to determine projected ROI based on your goals, so you can get buy-in and budget. We also offer customised pricing and subsidies based on team size and needs. Together we can demonstrate strong training ROI and a post training certificate for subjects.

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