Course description
In today's world, data is being generated at an unprecedented rate, and extracting valuable insights from this data has become a critical skill for businesses and organizations. This course is designed to help you develop the necessary skills and knowledge to tackle advanced data analysis challenges, ranging from exploratory data analysis to machine learning, time series analysis, Bayesian data analysis, and big data analytics.
Over the course of five days, you will learn about various techniques and tools used in the data analysis process, including data visualization, data modelling, and machine learning algorithms. You will also learn how to work with time series data, understand Bayesian statistics, and tackle big data analytics challenges using distributed computing tools such as Hadoop and Spark.
Throughout the course, you will have the opportunity to work on hands-on exercises using popular programming languages such as Python and libraries such as Scikit-learn, Statsmodels, and Spark MLlib. By the end of this course, you will have a strong foundation in advanced data analysis techniques that will enable you to solve complex data analysis challenges with confidence.
Upcoming start dates
Upcoming start dates
Suitability - Who should attend?
This course is ideal for individuals who have a basic understanding of data analysis and are looking to expand their knowledge and skills to tackle more complex and advanced data analysis tasks. This course is relevant for professionals from various industries, including finance, healthcare, marketing, and technology.
The course is suitable for data analysts, data scientists, business analysts, and anyone who works with data and wants to learn advanced techniques for data analysis. This course is also relevant for researchers, academics, and students who are looking to gain practical experience in data analysis techniques.
Additionally, individuals who are involved in decision-making and strategic planning will also benefit from attending this course. Managers, executives, and consultants who work with data will gain insights into how advanced data analysis techniques can be used to make informed decisions and drive business success.
Overall, this course is suitable for anyone who wants to gain a deeper understanding of advanced data analysis techniques and learn how to apply them to real-world data analysis challenges.
Outcome / Qualification etc.
- Develop a strong foundation in advanced data analysis techniques, including exploratory data analysis, machine learning, time series analysis, Bayesian data analysis, and big data analytics.
- Gain hands-on experience using popular data analysis tools and programming languages, such as Python, Scikit-learn, Statsmodels, and Spark MLlib.
- Learn how to tackle complex data analysis challenges, such as outlier detection, trend analysis, and predictive modeling, using advanced techniques and tools.
- Develop critical thinking and problem-solving skills that are necessary for working with large and complex datasets.
- Apply the knowledge and skills gained in this course to real-world data analysis projects, and confidently present findings and insights to stakeholders.
Training Course Content
Day 1
Exploratory Data Analysis
- Introduction to exploratory data analysis (EDA)
- Techniques for data visualization and exploration
- Measures of central tendency, dispersion, and correlation
- Outlier detection and treatment
- Hands-on exercises using Python libraries such as Pandas, Matplotlib, and Seaborn
Day 2
Machine Learning for Data Analysis
- Introduction to machine learning (ML) algorithms
- Supervised learning: regression, classification, decision trees, random forests
- Unsupervised learning: clustering, dimensionality reduction
- Cross-validation and hyperparameter tuning
- Hands-on exercises using Python libraries such as Scikit-learn and Keras
Day 3
Time Series Analysis
- Introduction to time series data
- Time series decomposition and trend analysis
- Seasonality and periodicity analysis
- Autoregressive Integrated Moving Average (ARIMA) models
- Hands-on exercises using Python libraries such as Statsmodels
Day 4
Bayesian Data Analysis
- Introduction to Bayesian statistics
- Bayes' theorem and probability distributions
- Bayesian modeling and inference
- Markov Chain Monte Carlo (MCMC) methods
- Hands-on exercises using Python libraries such as PyMC3 and Stan
Day 5
Big Data Analytics
- Introduction to big data and distributed computing
- MapReduce and Hadoop ecosystem
- Apache Spark and Spark SQL
- Machine learning on big data: Spark MLlib
- Hands-on exercises using tools such as Hadoop, Spark, and Databricks
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London Premier Centre
London Premier Centre is a UK leading training provider based in London and specialises in international short courses. Our inspiring, comprehensive portfolio of more than 400 professional development courses and seminars covers a wide range of professions from Administration, Leadership,...