Data Science Masters Course
USA Online, USA
Course
DURATION
24 weeks
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
EARLIEST START DATE
STUDY FORMAT
Distance Learning
Key Summary
About: The Data Science Masters Course is designed to equip students with the essential skills needed in today's data-driven world.
This program emphasizes practical experience with data analysis, statistical techniques, and machine learning.
Spanning one to two years, it offers a blend of theoretical knowledge and hands-on projects, ensuring graduates are ready to tackle real-world challenges effectively.
Career Outcomes: Graduates can explore various career paths, including data analyst, data scientist, machine learning engineer, and business intelligence analyst.
With the growing demand for data expertise, students are well-prepared for roles across industries, leveraging their skills to influence data-driven decision-making.
- Intensive 6-Month Program
- Collaborative Assignments with Mentors
- Master Statistics, Machine Learning, Deep Learning, and AI
- Learn Tools like Python, TensorFlow, Spark, R, and Tableau
DURATION
24 Weeks
EFFORT
10-15 Hrs/Week
CAREERS
Business Analyst, Data Analyst, Data Architect, Data Administrator, Data Manager, Data Scientist
The course teaches statistics for business analysis, machine learning algorithms, deep learning with TensorFlow, and programming with Python. It will help you explore, analyze, and interpret different kinds of data.
Why You Should Take This Course
Acadgild Experience
What You Will Learn in This Course
- Programming in Python and R
- Python & R basics
- Conditional and loops
- String and list objects
- Functions & OOPs concepts
- Exception handling
- Database programming
*Sessions on R are not live, but self-paced- Python & R basics
- Conditional and loops
- String and list objects
- Functions & OOPs concepts
- Exception handling
- Database programming
*Sessions on R are not live, but self-paced
- Data Wrangling
- Reading CSV, JSON, XML and HTML files using Python
- NumPy & pandas
- Scipy libraries
- Loading, cleaning, transforming, merging, and reshaping data
- Reading CSV, JSON, XML and HTML files using Python
- NumPy & pandas
- Scipy libraries
- Loading, cleaning, transforming, merging, and reshaping data
- Statistics and Probability
- Descriptive statistics
- Inferential statistics
- Hypothesis testing
- Statistical concepts in Python
- Descriptive statistics
- Inferential statistics
- Hypothesis testing
- Statistical concepts in Python
- Machine Learning Models with Python
- Building models using algorithms
- Linear and logistics regression
- Decision trees
- Support vector machines (SVMs)
- Random forests
- XGBoost
- K nearest neighbor & hierarchical clustering
- Principal component analysis
- Text analytics and time series forecasting
- Building models using algorithms
- Linear and logistics regression
- Decision trees
- Support vector machines (SVMs)
- Random forests
- XGBoost
- K nearest neighbor & hierarchical clustering
- Principal component analysis
- Text analytics and time series forecasting
- Deep Learning using TensorFlow
- Introduction to deep learning
- Understanding neural network through TensorFlow
- Convolution & recurrent neural networks
- Introduction to deep learning
- Understanding neural network through TensorFlow
- Convolution & recurrent neural networks
- Data Visualization using Matplotlib and Tableau
- Interactive visualizations with Matplotlib,
- Data visualizations using Tableau
- Tableau dashboard and storyboard
- Tableau and R integration
Sessions on Tableau are not live, but self-paced- Interactive visualizations with Matplotlib,
- Data visualizations using Tableau
- Tableau dashboard and storyboard
- Tableau and R integration
Sessions on Tableau are not live, but self-paced
- Handling Big Data with Spark
- Introduction to Big Data & Spark
- RDD's in Spark, data frames & Spark SQL
- Spark streaming, MLib & GraphX
- Introduction to Big Data & Spark
- RDD's in Spark, data frames & Spark SQL
- Spark streaming, MLib & GraphX