Data Science Master Certification
START DATE BATCH 1: 27th July, 2019 (Weekend batch)
START TIME: 09:30 AM TO 12:00 PM (EST)
START DATE BATCH 2: 31st August, 2019 (Weekend batch)
START TIME: 09:30 AM TO 12:00 PM (EST)
Data Science has become the new desirable IT job. While there are only few in the market conversant with the terms like Python, Machine Learning, Deep Learning and TensorFlow, it is also a fact that these skills are high in demand.
Ismart will transform you into a Data Scientist by delivering hands-on experience in Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, TensorFlow, Apache Spark, R and Tableau. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more. This 24 weeks long Data Science course has several advantages like 400 total coding hours and experienced industry mentors. This is the biggest advantage for understanding real world use cases and scenarios and applying theory in practice. Ismart Learning also ensures that real-time projects and case study discussions are facilitated to enhance learning. Icing on the cake is the lifetime access to our e-learning dashboard. This means you can log in and learn anytime about the latest technologies in the market
Michael Crawley: Holds a Degree with JOHNS HOPKINS University
Learning Pathway with Ismart Learning (Course Curriculum)
Programming in Python and R (10 HOURS- Live instructor classes)
This part of Data Science Masters program help you analyze data and to conduct data science. Below topics are covered in this Python essentials module for Data Science.
Sub topics of Python:
Core Python: Conditional Statement, Looping, Control Statement, String Manipulation, Lists, Tuple,
Dictionaries, Module, Input- Output, Exception handling.
Advanced Python: OOP Concept, CGI, Database, Networking, Multi-Threading.
- Getting Started: Basic R
- Working with Qualitative Data
- Working with Quantitative Data
- Documenting Work
- Finding Help
- Working with Two Variables
- The Data Frame
- Importing Data
Data Wrangling (20 HOURS– Live instructor classes)
This module will help users to work on messy, incomplete or complex data to make it usable using the below techniques.
- Reading CSV, JSON, XML and HTML files using Python
- NumPy & Pandas for working on large multidimensional arrays and matrices and for Data manipulation and analysis.
- Scipy libraries to provide mathematical algorithms and convenience functions built on the Numpy extension of Python.
Loading, Cleaning, Transforming, Merging, and Reshaping data.
- Design database schemas for efficient data representation
- Implement database schemas using MySQL
- Navigate data management issues in organizations
- Learn how to learn new technologies
- Learn the basics of programming in Python
- Import and export data to/from CVS and Excel, changing schemas as needed
- Conduct basic analyses in Excel
- Prepare a project workflow that imports data from different sources and produces reports
Statistics and Probability (10 HOURS– Live instructor classes)
Probability distributions, Statistical significance, Descriptive statistics, Inferential statistics,
Hypothesis testing and Regression.
AI and ML (Artificial Intelligence and Machine Learning)
Machine Learning Models with Python (30 HOURS– Live instructor classes)
Learn Regression, Classification, Clustering, Time Series, Dimensionality reduction and boosting Techniques using below Machine Learning algorithms.
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- KNN and Hierarchical Clustering
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boost & Adaboost
- Principal Component Analysis
- Text Analysis and Time Series forecasting
Deep Learning using TensorFlow (20 HOURS– Live instructor classes)
- Introduction to Deep Learning and TensorFlow
- Under TensorFlow: Linear regression, Nonlinear regression, Logistic regression and Activation functions
- Understanding of Neural Networks: CNN and RNN
- Deep dive into neural networks with TensorFlow
- Supervised and Unsupervised Learning
- Auto Encoders
Data Visualization using Matplotlib and Tableau (10 HOURS– Live instructor classes)
Data visualization is the technique to present the data in a pictorial or graphical format. It enables stakeholders and decision makers to analyze data visually. The data in a graphical format allows them to identify new trends and patterns easily.
Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib and Tableau.
Data Visualization with Matplotlib: Data visualization through Matplotlib through Bar charts, Pie charts, Histograms, Scatter plots, Stack plots and loading of the data from CSV and NumPy.
Data Visualization with Tableau:
- Installation of Tableau and Data visualizations
- Tableau Dashboard and Story board
- Tableau integration with R, Python and SQL server
Handling Big Data with Spark (15 HOURS– Live instructor classes)
- Introduction to Big Data & Spark
- Writing and deploying Spark applications, common patterns in spark data processing
- Data frames, Spark SQL and RDD’s in Spark
- Spark streaming, MLib & GraphX
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