Who is Data Scientist?
Before getting into the details of this course on “Become a Data Scientist”, first of all, let’s see who is Data Scientist and what are duties of data scientists.
Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role is to combine computer science, statistics, and mathematics. They also responsible for analyzing, process, and model data then interpret the results to create actionable plans for companies and other organizations.
Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions for uncovering solutions to business challenges. Data scientists are experts in making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.
A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data
What does a Data Scientist do?
Data scientists are responsible for implementing the following tasks such as:
- Collecting large amounts of data and analyzing it.
- Using data-driven techniques for solving business problems.
- Communicating the results to business and IT leaders.
- Spotting trends, patterns, and relationships within data.
- Converting data into compelling visualizations.
- Working with Artificial Intelligence and Machine Learning techniques.
- Deploying text analytics and data preparation.
About this course
In this “Become a Data Scientist course”, you will be taught whatever it needs to become a data scientist. From using principles of statistics and probability to design and execute A/B tests and recommendation engines to assist businesses in making data-automated decisions and deploying a data science solution to a basic flask app along with manipulation and analysis of distributed datasets using Apache Spark and also will be communicating results effectively to stakeholders.
What you will learn from this course?
You will master the skills necessary to become a Data Scientist. Also, you will work on projects designed by industry experts, and learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.
If you already have experience with machine learning, take this program.
Why should you enroll in this Course?
The data science field is expected to continue growing rapidly over the next several years, and there is always a huge demand for data scientists across industries. A data scientist is consistently rated as a top career option.
Udacity has collaborated with industry leaders to offer a world-class learning experience so you can advance your data science career. You will have hands-on experience running data pipelines, designing experiments, building recommendation systems, and more. Also, you will have personalized support as you master in-demand skills that qualify you for high-value jobs in the data science field.
Syllabus of ‘Become a Data Scientist’ Course
Course 1: Become a Data Scientist: Solving Data Science Problems
LESSON ONE: The Data Science Process
- Apply the CRISP-DM process to business applications.
- Wrangle, explore, and analyze a dataset.
- Apply machine learning for prediction and apply statistics for descriptive and inferential understanding.
- Draw conclusions that motivate others to act on your results.
LESSON TWO: Communicating with Stakeholders
- Implement best practices in sharing your code and written summaries.
- Learn what makes a great data science blog and how to create your ideas with the data science community.
Course 2: Become a Data Scientist: Software Engineering
LESSON ONE: Software Engineering Practices
- Write clean, modular, and well-documented code.
- Refactor code for efficiency and create unit tests to test programs.
- Write useful programs in multiple scripts along with track actions and results of processes with logging.
- Conduct and receive code reviews.
LESSON TWO: Object-Oriented Programming
- Understand when to use object-oriented programming.
- Build and use classes and understand magic methods.
- Write programs that include multiple classes, and follow good code structure.
- Learn how large, modular Python packages, such as pandas and scikit-learn, use object-oriented programming.
LESSON THREE: Wen Development
- Learn about the components of a web app.
- Build a web application that uses Flask, Plotly, and the Bootstrap framework.
- Portfolio Exercise: Build a data dashboard using a dataset of your choice and deploy it to a web application.
Course 3: Become a Data Scientist: Data Engineering
LESSON ONE: ETL Pipelines
- Understand what ETL pipelines are.
- Access and combine data from CSV, JSON, logs, APIs, and databases.
- Standardize encodings and columns.
- Normalize data and create a dummy variable and handle outliers, missing values, and duplicated data.
- Build an SQLite database to store cleaned data.
LESSON TWO: Natural Language Processing
- Prepare text data for analysis with tokenization, lemmatization, and removing stop words.
- Use scikit-learn to transform and vectorize text data and build features with a bag of words and tf-idf.
- Extract features with tools such as named entity recognition and part of speech tagging.
- Build an NLP model to perform sentiment analysis.
LESSON THREE: Machine Learning Pipelines
- Understand the advantages of using machine learning pipelines to streamline the data preparation and modeling process.
- Chain data transformations and an estimator with scikit- learn Pipeline.
- Use feature unions to perform steps in parallel and create more complex workflows.
- Grid search over the pipeline to optimize parameters for the entire workflow.
- Complete a case study to build a full machine learning pipeline that prepares data and creates a model for a dataset.
Course 4: Become a Data Scientist: Experiment Design and Recommendations
LESSON ONE: Experiment Design
- Understand how to set up an experiment, and the ideas associated with experiments vs. observational studies.
- Defining control and test conditions and choosing control and testing groups.
LESSON TWO: Statistical Concerns of Experimentation
- Applications of statistics in the real world.
- Establishing key metrics and SMART experiments: Specific, Measurable, Actionable, Realistic, Timely.
LESSON THREE: A/B Testing
- How it works and its limitations.
- Sources of Bias: Novelty and Recency Effects and multiple Comparison Techniques (FDR, Bonferroni, Tukey).
- Portfolio Exercise: Using a technical screener from Starbucks to analyze the results of an experiment and write up your findings.
LESSON FOUR: Introduction to Recommendation Engines
- Distinguish between common techniques for creating recommendation engines including knowledge-based, content-based, and collaborative filtering-based methods.
- Implement each of these techniques in python and list business goals associated with recommendation engines, and be able to recognize which of these goals are most easily met with existing recommendation techniques.
LESSON FIVE: Matrix Factorization for Recommendations
- Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
- Create recommendation engines using matrix factorization and FunkSVD and Interpret the results of matrix factorization to better understand latent features of customer data.
- Determine common pitfalls of recommendation engines like the cold start problem and difficulties associated with usual tactics for assessing the effectiveness of recommendation engines using usual techniques, and potential solutions.
Course 5: Become a Data Scientist: Data Science Projects
LESSON ONE: Elective 1: Dog Breed Classification
- Use convolutional neural networks to classify different dogs according to their breeds.
- Deploy your model to allow others to upload images of their dogs and send them back the corresponding breeds.
- Complete one of the most popular projects in Udacity history, and show the world how you can use your deep learning skills to entertain an audience!
LESSON TWO: Elective 2: Starbucks
- Use purchasing habits to arrive at discount measures to obtain and retain customers and identify groups of individuals that are most likely to be responsive to rebates.
LESSON THREE: Elective 3: Arvato Financial Services
- Work through a real-world dataset and challenge provided by Arvato Financial Services, a Bertelsmann company.
- Top performers have a chance at an interview with Arvato or another Bertelsmann company!
LESSON FOUR: Elective 4: Spark for Big Data
- Take a course on Apache Spark and complete a project using a massive, distributed dataset to predict customer churn.
- Learn to deploy your Spark cluster on either AWS or IBM Cloud.
LESSON FIVE: Elective 5: Your Choice
- Use your skills to tackle any other project of your choice.
What jobs will this program prepare you for?
Obtaining the skills required to be a Data Scientist will make you extremely valuable across many industries and in many roles. Data Scientists work as Analysts, Statisticians, Engineers, and more. Some become Data and Analytics Managers, while others specialize as Database Administrators. After the competition of this Nano-degree program, you will have the proficiency to seek out roles that run the gamut from generalist to specialist, and all points in between.
This Nano-degree Program Includes:
- Experienced Project reviews.
- Technical mentor support.
- Personal career services.
How is the Nano-degree program structured?
The Data Analyst Nano-degree program is comprised of content and curriculum to support five (5) projects. They also estimated that students can complete the program in four (4) months working 10 hours per week. Each project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Prerequisites for ‘Become a Data Scientist Course’
In order to successfully complete this program, you should meet the following prerequisites:
- Python programming, including common data analysis libraries (NumPy, pandas, Matplotlib).
- SQL programming and statistics (Descriptive and Inferential).
- Calculus, Linear Algebra, and experience with wrangling and visualizing data
If you do not meet the requirements to enroll, What should you do?
Udacity has a number of Nano-degree programs and free courses that can help you prepare, such as:
- Introduction to Data Science.
- Introduction to Python.
- SQL for Data Analysis, Statistics, and Linear Algebra.
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- Bertelsmann Figure eight an appen co. IBM Insight Kaggle Starbucks
- 3+ Months
- Paid Course (Paid certificate)
- Data Engineering Data Science Machine learning Natural language processing Spark