616 Courses

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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.

Subject content Students will draw upon a range of texts as reading stimulus and engage with creative as well as real and relevant contexts. Students will have opportunities to develop higher-order reading and critical thinking skills that encourage genuine enquiry into different topics and themes. We can help students to read fluently and write effectively. Students will be able to demonstrate a confident control of Standard English and write grammatically correct sentences, deploying figurative language and analysing texts. For GCSE English Language students should: read fluently, and with good understanding, a wide range of texts from the 19th, 20th and 21st centuries, including literature and literary non-fiction as well as other writing such as reviews and journalism read and evaluate texts critically and make comparisons between texts summarise and synthesise information or ideas from texts use knowledge gained from wide reading to inform and improve their own writing write effectively and coherently using Standard English appropriately use grammar correctly and punctuate and spell accurately acquire and apply a wide vocabulary, alongside a knowledge and understanding of grammatical terminology, and linguistic conventions for reading, writing and spoken language listen to, and understand, spoken language and use spoken Standard English effectively. Texts GCSE English Language is designed on the basis that students should read and be assessed on high-quality, challenging texts from the 19th, 20th and 21st centuries. Each text studied represents a substantial piece of writing, making significant demands on students in terms of content, structure and the quality of language.  The texts, across a range of genres and types, support students in developing their own writing by providing effective models.  The texts include literature and extended literary non-fiction, and other writing such as essays, reviews and journalism (both printed and online). We can provide assistance for everything you need to prepare students for exams, including: past papers, mark schemes and examiners’ reports specimen papers and mark schemes for new courses exemplar student answers with examiner commentaries guidance in planning and writing cohesively high quality revision guides

Unlocking Success in GCSE Geography! At GLA Tutors, we are dedicated to helping students excel in their GCSE Geography examinations. Our experienced tutors are passionate about the subject and committed to providing comprehensive support tailored to the AQA examination board's specification. Let's dive into the breakdown of the AQA GCSE Geography specification: Paper 1: Living with the Physical Environment This paper focuses on understanding natural landscapes, such as rivers, coasts, and ecosystems. Our tutors will guide students through topics like the water cycle, coastal processes, and the impact of climate change. We offer in-depth explanations, interactive activities, and exam-style practice to build a solid foundation. Paper 2: Challenges in the Human Environment This paper explores the relationship between humans and their environment, including urban areas, development, and global issues. Our tutors will delve into concepts like population dynamics, urbanisation, sustainable development, and global inequalities. Through engaging discussions and real-world examples, we help students grasp the complexities of human geography. Paper 3: Geographical Applications In this paper, students apply their geographical skills to investigate real-world issues and carry out fieldwork. Our tutors will guide students through the process of designing and conducting fieldwork, collecting and analysing data, and presenting their findings. We provide practical guidance, research resources, and feedback to develop strong investigative skills. At GLA Tutors we go beyond the specification to nurture a deep understanding of geography. Our tutors create a supportive and engaging learning environment that encourages critical thinking, analysis, and effective exam techniques. We offer personalised one-on-one sessions, group discussions, and access to a range of learning materials to cater to each student's needs. Whether it's understanding the intricacies of physical processes or analysing the complexities of human interactions, our tutors are here to guide students towards success in their GCSE Geography journey. Join us and unlock your full potential in GCSE Geography! Feel free to explore our website for more information or reach out to us with any questions you may have. Let's embark on this exciting learning adventure together!  https://www.globallearners.academy/services/gcse We can provide assistance for everything you need to prepare students for exams, including: past papers, mark schemes and examiners’ reports specimen papers and mark schemes for new courses exemplar student answers with examiner commentaries high quality revision guides

Excelling in GCSE Religious Studies! At GLA Tutors, we are dedicated to helping students succeed in their GCSE Religious Studies examinations. Our experienced tutors are passionate about the subject and committed to providing comprehensive support aligned with the AQA examination board's specification. Let's explore the breakdown of the AQA GCSE Religious Studies specification: Paper 1: The Study of Religions: Beliefs and Teachings This paper focuses on the study of two religions, such as Christianity and Islam. Our tutors will guide students through the fundamental beliefs, teachings, and practices of these religions. We delve into topics like the nature of God, religious texts, worship, and the impact of religion on individuals and society. Through engaging discussions and thought-provoking exercises, we help students develop a deep understanding of religious beliefs. Paper 2: Thematic Studies This paper explores ethical and philosophical issues, as well as the influence of religion in the modern world. Our tutors will delve into topics like crime and punishment, human rights, life and death, and religion and society. We provide in-depth analysis, case studies, and perspectives from different religious traditions to enable students to critically examine these issues. We also emphasise the development of strong argumentation and evaluation skills. Paper 3: Study of Religion: Textual Studies In this paper, students will explore religious texts and their significance. Our tutors will guide students through the study of sacred texts, such as the Bible or the Qur'an. We help students analyse and interpret these texts, understand their historical and cultural context, and explore their relevance in contemporary society. We provide comprehensive guidance on textual analysis and the application of religious teachings to real-life situations. At GLA Tutors, we foster a supportive and inclusive learning environment, where students can explore and deepen their understanding of religious studies. Our tutors provide personalised one-on-one sessions, group discussions, and access to a range of learning resources to cater to each student's unique needs. Beyond the specification, we encourage critical thinking, empathy, and open-mindedness, enabling students to engage with complex ethical and philosophical questions. We also focus on developing strong exam techniques, essay writing skills, and effective revision strategies to maximize exam success. Join us at GLA Tutors and embark on a transformative journey in GCSE Religious Studies. Our tutors are here to guide you towards academic excellence, a deep appreciation for religious diversity, and the ability to apply religious teachings to real-world contexts. Feel free to exploreour websitefor more information or reach out to us with any questions you may have.  https://www.globallearners.academy/services/gcse We can provide assistance for everything you need to prepare students for exams, including: past papers, mark schemes and examiners’ reports specimen papers and mark schemes for new courses exemplar student answers with examiner commentaries high quality revision guides

Getting to grips with GCSE History At GLA Tutors, we are dedicated to helping students excel in their GCSE History examinations. Our experienced tutors are passionate about history and committed to providing comprehensive support aligned with the AQA examination board's specification.  Let's explore the breakdown of the AQA GCSE History specification: Paper 1: Understanding the Modern World This paper focuses on key historical events and developments from the 20th century. Our tutors will guide students through topics such as the origins of World War I, the Treaty of Versailles, the rise of Hitler and the Nazis, the Cold War, and the civil rights movement. We provide in-depth analysis, engaging discussions, and access to a wide range of historical sources to help students develop a deep understanding of these crucial events. Paper 2: Shaping the Nation In this paper, students will explore the history of Britain from medieval times to the present day. Our tutors will delve into topics such as the Norman Conquest, the Tudors, the Industrial Revolution, the British Empire, and the impact of immigration. We provide comprehensive guidance on key historical figures, significant events, and the social, political, and economic changes that shaped the nation. Through interactive lessons and engaging activities, we help students develop a strong grasp of British history. Historical Investigation This component allows students to conduct an in-depth investigation on a topic of their choice. Our tutors will provide guidance on selecting a suitable topic, conducting research, analysing sources, and presenting findings. We help students develop critical thinking skills, research methodology, and the ability to construct coherent and well-supported arguments. This component allows students to develop their historical research skills while exploring a topic of personal interest. At GLA Tutors, we foster a supportive and inclusive learning environment, where students can explore and deepen their understanding of history. Our tutors provide personalised one-on-one sessions, group discussions, and access to a range of learning resources to cater to each student's unique needs. Beyond the specification, we encourage critical thinking, historical empathy, and the ability to analyse and interpret historical sources. We also focus on developing strong exam techniques, essay writing skills, and effective revision strategies to maximise exam success. Join us at GLA Tutors and embark on a transformative journey in GCSE History. Our tutors are here to guide you towards academic excellence, a deeper appreciation for the past, and the ability to critically analyse historical events and their impact on the world today. Feel free to explore our website for more information or reach out to us with any questions you may have. We can provide assistance for everything you need to prepare students for exams, including: past papers, mark schemes and examiners’ reports specimen papers and mark schemes for new courses exemplar student answers with examiner commentaries high quality revision guides

This course aims to teach you how to employ NLP for self-improvement by examining exclusive tactics and implementing them to meet one’s goals.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

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. 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. Additional course details: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 Primer: Hands-on Technical Overview (TTPS4872) 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.