617 Maths courses

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.

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. Overview After completing this course delegates will be capable of writing effective R code to manipulate, analyse and visualise data to enable their organisations make better, data-driven decisions. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Course Outline Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. The R programming language is one of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions. The course will explore the following topics through a series of interactive workshop sessions: What is R? Basic R programming conventions Data structures in R Accessing data in R Descriptive statistics in R Statistical analysis in R Data manipulation in R Data visualisation in R Additional course details: Nexus Humans Beginning Data Analytics With R 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 Beginning Data Analytics With R 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 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.

Welcome to GLA Tutors, your go-to platform for GCSE English tutoring. At GLA Tutors, we understand the significance of excelling in GCSE English and strive to provide comprehensive and customised support to help students achieve outstanding results. Our team of experienced tutors is well-versed in the AQA examination board's specification for GCSE English. We have meticulously analysed the AQA specification to ensure that our tutoring services align with the curriculum requirements and cover all the essential elements and skills. The AQA GCSE English specification consists of two main components: 1. English Language: - Paper 1: Explorations in Creative Reading and Writing - Paper 2: Writers' Viewpoints and Perspectives - Spoken Language Endorsement 2. English Literature: - Paper 1: Shakespeare and the 19th-century novel - Paper 2: Modern Texts and Poetry Our tutors at GLA Tutors possess a deep understanding of each component and are equipped with the knowledge and resources to provide comprehensive support in all areas. Whether it's analysing texts, honing writing skills, or developing effective communication skills, our tutors are dedicated to helping students excel in their GCSE English exams. We believe in a personalised approach to tutoring. We understand that every student has unique learning needs and preferences. Our tutors tailor their teaching methods to accommodate individual learning styles, ensuring that each student receives the support and guidance they need to succeed. Our tutoring sessions are interactive and engaging. We foster a supportive learning environment where students are encouraged to ask questions, participate in discussions, and develop critical thinking skills. Our tutors employ a range of teaching techniques, including close textual analysis, essay writing practice, and creative writing exercises, to help students master the key skills required for GCSE English. With GLA Tutors, you can be confident that you will receive top-quality tutoring in GCSE English. Our tutors are committed to helping you unlock your full potential, providing guidance, and instilling confidence in your ability to excel in your exams. Join us and let us help you achieve outstanding success in GCSE English. 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

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

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 explore our website for more information or reach out to us with any questions you may have. Let's embark on this enriching educational adventure together! 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

Duration 2 Days 12 CPD hours This course is intended for This course is aimed at anyone currently working with data who is interested in using data visualisation to more effectively communicate their results. Overview At completion, delegates will understand how data visualisations can be best used to communicate actionable insights from data and be competent with the tools required to do it. Visualising data, and analytics results, is one of the most effective ways to achieve this. This course will cover the theory of data visualisation along with practical skills for creating compelling visualisations from data. Course Outline The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to horse breeding. The companies doing this most successfully understand that using sophisticated analytics approaches to unlock insights from data is only half the job. Communicating these insights to all of the different parts of an organisation is just as important as doing the actual analysis. Visualising data, and analytics results, is one of the most effective ways to achieve this. This course will cover the theory of data visualisation along with practical skills for creating compelling visualisations from data. To attend this course delegates should be competent in the use of data analysis tools such as reporting tools, spreadsheet software or business intelligence tools. The course will explore the following topics through a series of interactive workshop sessions: Fundamentals of data visualisation Data characteristics & dimensions Mapping visual encodings to data dimensions Colour theory Graphical perception & communication Interaction design Visualisation different characteristics of data: trends, comparisons, correlations, maps, networks, hierarchies, text Designing effective dashboards

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

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