Get started with the second course in the Growth Product Manager Nanodegree program. Hone specialized Product Management skills in activation and retention strategy by learning how to guide users through the activation funnel as fast as possible, so they reach your product’s aha-moment. Apply the fundamentals of increasing customer engagement, and learn how to analyze cohorts to identify key factors for retention. You will also deploy experiments to improve customer lifetime value and decrease churn rate.

Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.

Perform analysis on data stored in relational and non-relational database systems to power strategic decision-making. Learn to determine, create, and execute SQL and NoSQL queries that manipulate and dissect large scale datasets. Begin by leveraging the power of SQL commands, functions, and data cleaning methodologies to join, aggregate, and clean tables, as well as complete performance tune analysis to provide strategic business recommendations. Finally, apply relational database management techniques to normalize data schemas in order to build the supporting data structures for a social news aggregator.

Learn how to process data in real-time by building fluency in modern data engineering tools, such as Apache Spark, Kafka, Spark Streaming, and Kafka Streaming. You’ll start by understanding the components of data streaming systems. You’ll then build a real-time analytics application. Students will also compile data and run analytics, as well as draw insights from reports generated by the streaming console.

You’ll master the in-demand skills necessary to become a successful data analyst like data pre-processing, visualization and analysis using Power BI as the primary tool.

This course provides business leaders and managers with strategies and guidelines for how best to solve the human capital, technological, and management challenges of building data science into the business. Students will gain skills in identifying opportunities for data science across many functional areas of the business, as well as learn the tools to prioritize and execute on those opportunities as part of a data science initiative.

Learn back-end development with the Java programming language, one of the most popular programming languages in the world and one a majority of large enterprises rely on for their infrastructure.

• Install Tableau Desktop 2020
• Connect Tableau to various Datasets: Excel and CSV files
• Create Barcharts
• Create Area Charts
• Create Maps
• Create Scatterplots
• Create Piecharts
• Create Treemaps
• Create Interactive Dashboards
• Create Storylines
• Understand Types of Joins and how they work
• Work with Data Blending in Tableau
• Create Table Calculations
• Work with Parameters
• Create Dual Axis Charts
• Create Calculated Fields
• Create Calculated Fields in a Blend
• Export Results from Tableau into Powerpoint, Word, and other software
• Work with Timeseries Data (two methods)
• Creating Data Extracts in Tableau
• Understand Aggregation, Granularity, and Level of Detail
• Adding Filters and Quick Filters
• Create Data Hierarchies
• Adding Actions to Dashboards (filters & highlighting)
• Assigning Geographical Roles to Data Elements
• Advanced Data Preparation (including latest updates in Tableau)

Leverage market data to amplify product development. Learn how to apply data science techniques, data engineering processes, and market experimentation tests to deliver customized product experiences. Begin by leveraging the power of SQL and Tableau to inform product strategy. Then, develop data pipelines and warehousing strategies that prepare data collected from a product for robust analysis. Finally, learn techniques for evaluating the data from live products, including how to design and execute various A/B and multivariate tests to shape the next iteration of a product.

Learn the programming fundamentals required for a career in data science. By the end of the program, you will be able to use Python, SQL, Command Line, and Git.

• Cell phone science: If you are an avid user of your cell phone, try downloading several months of your calling data. You may see daily and long-term patterns in your usage that surprise you. Plus, any changes in your routine, such as a vacation, will show up prominently.
• Hardball analytics: The book and film Moneyball tell how the Oakland A’s overcame one of the smallest budgets in major league baseball to assemble a division-winning team. The secret? Managers used overlooked data analytics to hire undervalued, high-performing players.
• Presidential prediction: In the 2012 presidential election, statistician Nate Silver and a few others correctly predicted the winner of all 50 states and the District of Columbia. Here, weighting criteria make it possible to analyze data collected by hundreds of pollsters from thousands of distinct polls.

What you will learn

• How to bridge diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing
• Advanced techniques to analyze genomic data.
• How to structure, annotate, normalize, and interpret genome-scale assays.
• How to analyze data from several experimental protocols, using open-source software, including R and Bioconductor.

What you will learn

• Fundamental R programming skills
• Statistical concepts such as probability, inference, and modeling and how to apply them in practice
• Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
• Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
• Implement machine learning algorithms
• In-depth knowledge of fundamental data science concepts through motivating real-world case studies

In this program, you’ll learn foundational data skills that apply across functions and industries. You’ll learn to analyze data and build models with Excel, query databases using SQL, and create informative data visualizations with Tableau.

Python is a general-purpose coding language with applications in web development, data science, machine learning, fintech and more. The Intermediate Python Nanodegree program equips you to leverage the capabilities of Python and streamline the functionality of applications that perform complex tasks, such as classifying files, data mining a webpage, etc. By the end of the program, you’ll have a portfolio that demonstrates your ability to apply practitioner-level Python skills on the job.

Learning Objectives:

• Data analysis skills
• Analyzing your organization's data
• Approach data analysis

• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Handle advanced techniques like Dimensionality Reduction
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Advance your programming skills and refine your ability to work with messy, complex datasets. You’ll learn to manipulate and prepare data for analysis, and create visualizations for data exploration. Finally, you’ll learn to use your data skills to tell a story with data.

• Have a fundamental understanding of the Python programming language.
• Have the skills and understanding of Python to confidently apply for Python programming jobs.
• Acquire the pre-requisite Python skills to move into specific branches - Machine Learning, Data Science, etc..
• Add the Python Object-Oriented Programming (OOP) skills to your résumé.
• Understand how to create your own Python programs.
• Learn Python from experienced professional software developers.
• Understand both Python 2 and Python 3.

• The course provides 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
• 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 TensorFlowDevelop 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

• The course provides 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
• 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 TensorFlowDevelop 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

• Become an expert in Statistics, SQL, Tableau, and problem solving
• Boost your resume with in-demand skills
• Gather, organize, analyze and visualize data
• Use data for improved business decision-making
• Present information in the form of metrics, KPIs, reports, and dashboards
• Perform quantitative and qualitative business analysis
• Analyze current and historical data
• Discover how to find trends, market conditions, and research competitor positioning
• Understand the fundamentals of database theory
• Use SQL to create, design, and manipulate SQL databases
• Extract data from a database writing your own queries
• Create powerful professional visualizations in Tableau
• Combine SQL and Tableau to visualize data from the source
• Solve real-world business analysis tasks in SQL and Tableau

Learning Objectives:

• Think about data visualization
• How to communicate with data to any audience
• Visual Perception
• Chart selection

• Understand data analysis via EDA as a journey and a way to explore data
• Explore data at multiple levels using appropriate visualizations
• Acquire statistical knowledge for summarizing data
• Demonstrate curiosity and skepticism when performing data analysis
• Develop intuition around a data set and understand how the data was generated.

In this program, you’ll plan, design and implement enterprise data infrastructure solutions and create the blueprints for an organization’s data management system. You’ll create a relational database with PostGreSQL, design an Online Analytical Processing (OLAP) data model to build a cloud based data warehouse, and design scalable data lake architecture that meets the needs of Big Data. Finally, you’ll learn how to apply the principles of data governance to an organization’s data management system.