Upcoming Python Data Science Courses in London
Data Science Course Information
- Why data science?
- Applications of data science
- Why Python for data science?
- Introduce different packages – Numpy, Pandas, Matplotlib, Scikit-learn
- Setup environment (install Anaconda and toy example datasets for practice tasks)
- Identify what tutees will need data science for
- Inputting/storing data (reading CSVs and other file types)
- Cleaning data (what happens if there are missing values?)
- Unstructured data (Pandas Dataframes)
- Introduction to Python with SQL Databases
- Different plots (line, bar, box, scatter) in Matplotlib
- Editing graphs (styles, colours, labels etc.)
- Calculating averages, variance etc.
- Probability distributions (normal, binomial, poisson)
- Hypothesis testing
- Supervised (linear regression, neural networks, decision trees, naïve bayes, k nearest neighbours)
- Introduction to Tensorflow and Pytorch
- Unsupervised (clustering, outlier detection)
- Testing to improve accuracy (using statistics)
- Visualising machine learning models
- How to explain the workings of machine learning models
- Combining processing, visualising, statistics and machine learning lessons
- Practice tasks
- Answering any questions and identify real business needs
What is Data Science?
It is no secret that data has taken over the world. From social media to e-commerce, organisations are collecting more data than ever before. In order to make sense of all this data and gain insights that can help them make better business decisions, they need people who can analyse this data and find trends and patterns – enter Data Science.
In fact, a recent survey found that a high percentage of executives expect their organisations to have at least one data scientist in the next five years.
But what exactly is Data Science? What do you need to study in order to become a data scientist? How is the field evolving? We answer all these questions and more in our comprehensive guide below.
Data Science is the process of using data to solve problems. It can be used in a variety of industries, including marketing, finance, and healthcare. Data science involves understanding how data works and extracting insights from it. This knowledge can then be used to create models or algorithms that can help make decisions.
By understanding how data works and using techniques like predictive analytics and deep learning, businesses are able to identify patterns and trends that would otherwise be difficult or impossible to see.
Types of Data Scienctist
There are many different types of data scientists, each with their own specialised skills and knowledge. Some common skills include:
- Statistics: Statistics is essential for understanding how data behaves and analysing its significance. It helps us understand patterns in our data so we can find trends and predict future outcomes.
- Data acquisition – Collecting raw data from various sources (e.g., sensors or online surveys) for analysis and modelling purposes
- Data cleaning – Identifying and correcting errors in the data before it’s analysed
- Data preparation – transforming raw data into a form that can be easily analysed by machine learning algorithms or other analytical methods
- Machine learning: Machine learning is a type of statistical analysis that uses artificial intelligence (AI) to learn on its own by training on large datasets.
Why Python for Data Science?
Python is a simple yet powerful language with a large collection of libraries, which can be used for data manipulation and analysis. This makes it an ideal language for data science.
This is why it is important that students attending the Data Science course have a strong foundation in Python.
What do we need Data Science for?
From predicting consumer behaviour to automating business processes, data science is helping organisations make better decisions and solve problems more efficiently.
Here are some of the reasons why you should learn or develop your skills in data science:
- Improve Customer Services – It helps you improve your service delivery levels and make sure that it meets the needs of your customers properly and efficiently.
- Automation – Machine learning uses algorithms that are trained on large amounts of data in order to learn from it automatically. This allows systems to “learn” how best to perform certain tasks or solve problems without being explicitly programmed
- To understand your customers better – By understanding your customers’ behaviour over time (e.g., what products they buy or how often they contact you), you can create more targeted ads or improve your customer service strategy.
- To develop new products faster – This also allows you to launch new products on a schedule that makes sense for your business rather than waiting until an idea “comes together”.
- To reduce risk – By predicting how customers will behave in the future based on past behaviour, you can avoid making mistakes that could damage your business (e.g., by releasing a faulty product).
What is Machine Learning?
There are two types of machine learning: supervised and unsupervised:
- Supervised learning: is where we have a set of examples, and we want to learn how to predict values for new data points. For example, if we want to predict whether a new customer will buy our product, we can use all of our past customers’ purchase histories as examples. In this case, we would provide the model with all their purchase histories and let it try to predict new purchases.
- Unsupervised learning is where we don’t have any examples or training data available. For example, if we want to build an algorithm that finds patterns in images like those shown above (and much more), then it needs to figure out how these images are related by themselves without any knowledge about what they have in common or what makes one pattern different from another.
Examples of Data Science Problems
Data science has become an increasingly important field as businesses grapple with big data challenges such as managing fraud or understanding customer behaviour.
Here are some real-world examples of data science problems:
- Detecting fraudulent activity on a website
- Predicting consumer behaviour based on past purchases
- Analysing large text corpora for sentiment analysis
A Few more practical examples are:
- Marketing research: Marketers use data science to study customer behaviour and preferences in order to create targeted campaigns.
- Business intelligence: Data scientists use data analysis to improve company performance by identifying trends and patterns. This information can then be used for strategic planning or decision-making.
- Machine learning: Machine learning algorithms learn from data alone and are able to “learn” on their own without being explicitly programmed; this makes them very powerful tools for predicting future outcomes based on past experiences (like sales).
Job prospects in the field of Data Science
As the world becomes increasingly data-driven, there is a growing need for people with skills in data science. According to the chronicle, the number of jobs in this field will grow by 31% between now and 2026.
Here are some job prospects you may want to keep an eye out for:
- Data Analyst: Data analysts work with raw data collected from various sources, such as customer surveys or online sales transactions, to develop insights that can be used to improve business operations.
- Data Engineer: A data engineer designs and implements solutions that enable businesses to extract value from their massive amounts of data.
- Machine Learning Engineer: Machine learning engineers use algorithms that learn how to make predictions based on large sets of training examples (data).
- Database administrator: A database administrator (DBA) is responsible for maintaining an organisation’s databases – including both internal systems used by employees and third-party applications accessed by customers.
- Data scientists: These professionals work with and analyse large amounts of data to find patterns and insights.
- Software developers: Those working in this field build software applications that utilise (or integrate) machine learning techniques. This includes everything from developing front-end code to creating back-end systems that interact with databases or other external sources of information
How is the field of Data Science evolving?
In recent years, data science has become increasingly important in both commercial and academic settings.
The field of data science is constantly evolving; new methods and tools are being developed all the time to make it easier for professionals in this field to work more effectively with big datasets. As a result, data scientists often need strong mathematical skills as well as knowledge in computer programming languages like Python or R.