Complete Data Science Bootcamp
What is Data science?
Data science is the field of study that combines domain expertise, programming skills, and knowledge
of mathematics and statistics to extract meaningful insights from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value. Day by day more and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.
About Data Science Bootcamp Program
A data scientist is one of the best-suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
Universities have been slow at creating specialized data science programs. Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
This course will definitely assist you to move towards a hot career path in data science, However data science is a multidisciplinary field. It encompasses a wide range of topics which includes:
- Understanding of the data science field and the type of analysis carried out
- Mathematics, Statistics, and Python.
- Applying advanced statistical techniques in Python.
- Data Visualization, Machine Learning, and Deep Learning.
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, time-efficient, and structured data science training available online, Udemy has created The Data Science Course 2020.
Also as being Udemy’s first training program it solves the biggest challenge to entering the data science field. In this data science bootcamp, you will be able to get all the necessary resources in one place.
Complete Data Science bootcamp program includes:
- Intro to Data and Data Science
- Advanced Statistics
- Machine Learning
What you will learn from this course?
- The course provides you the entire toolbox you need to become a data scientist.
- Fill up your resume with in-demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow.
- Impress interviewers by showing an understanding of the data science field.
- Learn how to pre-process data.
- You will understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!).
- Start coding in Python and learn how to use it for statistical analysis.
- Perform linear and logistic regressions in Python.
- Carry out cluster and factor analysis.
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels, and scikit-learn.
- Apply your skills to real-life business cases.
- Use state-of-the-art Deep Learning frameworks such as Google’s Tensor Flow Develop a business intuition while coding and solving tasks with big data.
- Unfold the power of deep neural networks.
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross-validation, testing, and how hyperparameters could improve performance.
- Warm-up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
About the Instructors
Data Scientist | Android Developer | Teacher
Philipp is data scientist and mobile developer with a passion for teaching and the lead instructor at the London App Brewery for machine learning and Android development, fluent in Python, Java, Swift, Dart, and VBA.
Developer and Lead Instructor
Angela is a developer with a passion for teaching and the lead instructor at the London App Brewery, London’s leading Programming Bootcamp also they have helped hundreds of thousands of students learn to code and change their lives by becoming a developer.
The Data Science Bootcamp course consists of the following 7 parts:
Part 1 – Introduction
- The Field of Data Science – The Various Data Science Disciplines, connecting the Data Science Disciplines, The Benefits of each Discipline, Popular Data Science Techniques, and Popular Data Science Tools.
Part 2 – Probability
- Basics of Probability.
- Bayesian Inference.
- Probability in other Fields.
Part 3 – Statistics
- Descriptive Statistics.
- Inferential Statistics Fundamentals and Confidence Intervals.
- Hypothesis Testing.
Part 4 – Introduction to Python
- Basics of Python and Python Syntax
- Variables and Data Types
- Other Python Operators
- Conditional Statements
- Python Functions
- Advanced Python Tools
Part 5 – Advanced Statistical methods in Python
- Linear Regression with statsmodels.
- Multiple Linear Regression with statsmodels.
- Linear Regression with sklearn.
- Practical Example: Linear Regression.
- Logistic Regression and Cluster Analysis.
- K-Means Clustering.
- Other Types of Clustering.
Part 6 – Mathematics
- Scalars and Vectors and Linear Algebra and Geometry.
- Arrays in Python – A Convenient Way To Represent Matrices.
- Addition and Subtraction of Matrices and Errors when Adding Matrices.
- Transpose of a Matrix and Dot Product and Dot Product of Matrices.
The above course content from the Data Science Bootcamp emphasizes statistics, probability, mathematics, and the use of python for the data analysis. Further, Part 7 is dedicated to Deep Learning and an example of a business case.
Part 7 – Deep Learning
- Introduction to Neural Network.
- How to Build a Neural Network from Scratch with NumPy.
- Tensorflow 2.0: Introduction.
- Digging Deeper into NNs: Introduction Deep Neural Network.
- Overfitting and Initialization.
- Digging into Gradient Descent and Learning rate schedules.
- Classifying on the MNIST Dataset.
- Business case Example.
- TensorFlow 1: Introduction, Classifying on the MNIST Dataset and Business case.
Who this course is for:
- You should take this course if you want to become a Data Scientist or if you want to learn about the field.
- This course is for you if you want a great career.
- The course is also ideal for beginners, as it starts with the fundamentals and gradually builds up your skills.
- No prior experience is required. You will start with the very basics.
- You’ll need to install Anaconda. You will be showed how to do that step by step.
- Microsoft Excel 2003, 2010, 2013, 2016, or 365.
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
- About our policies and review criteria.
- How can you choose and compare online courses?
- How to add Courses to your Wishlist?
- You can suggest courses to add to our website.
- Jam-packed with a good balance of technical and practical information.
- Extremely eye-catching design and presentation. The way content is delivered on the screen is very attractive.
- The exercises are very informative, exhaustive and detailed.
- One can commence without any prior programming experience.
- Realistic approach, balanced theories and practice.
- Need to expand details on other classifiers such as knn, SVMs and decision trees.
- Need to add basic mathematics calculus.
- Lengthy content on topics such as probability and stats.
Specification: Complete Data Science Bootcamp