ACADGILD
Data Science Masters Course

Online, USA

DEGREE TYPE

Course

DURATION

24 weeks

LANGUAGES

English

PACE

Full time, Part time

APPLICATION DEADLINE

EARLIEST START DATE

STUDY FORMAT

Distance Learning

Key Summary

About: This intensive 6-month (24-week) Data Science Masters Course from AcadGild teaches statistics, machine learning, deep learning (TensorFlow), Python and R programming, Spark and Tableau. You'll learn data wrangling with NumPy and pandas, statistical inference, supervised and unsupervised models, text analytics, time-series forecasting, deep neural networks (CNNs, RNNs), data visualization and big data processing. The course uses collaborative assignments with mentors and requires 10–15 hrs/week of effort.

Career Outcomes: Graduates can pursue roles as Business Analyst, Data Analyst, Data Architect, Data Administrator, Data Manager, Data Scientist, or related positions such as machine learning engineer, AI engineer, analytics consultant and researcher.

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
Data Wrangling
- 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
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
Deep Learning using TensorFlow
- 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
Handling Big Data with Spark
- Introduction to Big Data & Spark
- RDD's in Spark, data frames & Spark SQL
- Spark streaming, MLib & GraphX