Data Science
-
Excel in Data Science, one of the hottest fields in tech today. Learn how to gain new insights from big data by asking the right questions, manipulating data sets and visualizing your findings in compelling ways.
In this MicroMasters data science training course program, you will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations.
This data science course encompasses two sides of data science learning: the mathematical and the applied. We provide the course online as well as in classroom.
Data Science has three board domains viz., Statistics, Machine Learning/ Programming/ Data Skills, Business Domain Knowledge. The foundation of data science course focuses on basic statistics, overview of Machine Learning with hands on practice with couple of ML algorithms and application of Data Science in top 5 industry domains.
This also includes two hands-on Data Science case study, enabling the candidates to not only understand the core concepts but also gain practical hands on knowledge, thereby boosting the confidence to pursue further knowledge in the field of Data Science.
- WHAT YOU WILL LEARN
-
Key Learnings :
- Understand the big picture of Data Science, how and where you/ your organisation can benefit from implementing Data Science
- Essential knowledge on Statistics related to Data Science
- Learned to perform data analysis for large set of data using various tools such as R, Python, Tableau etc.,
- Understand what is Machine Learning, Artificial Intelligence and various methods to deploy them for business data
- If you aspire to be a Data Scientist, you will gain a clear road map to become one.
- Gain a foundational understanding of business analytics
- Install R, R-studio, and workspace setup, and learn about the various R packages
- Master R programming and understand how various statements are executed in R
- Gain an in-depth understanding of data structure used in R and learn to import/export data in R
- Define, understand and use the various apply functions and DPLYP functions
- Understand and use the various graphics in R for data visualization
- Gain a basic understanding of various statistical concepts
- Understand and use hypothesis testing method to drive business decisions
- Understand and use linear, non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules and Apriori algorithm
- Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
- COURSE CURRICULUM
-
Topics Covered During Classroom :
1. Overview of Data Science
- Source of Big Data
- How to Handle Big Data
- What is Data Science, Artificial Intelligence and Machine Learning
- Statistical Learning and Machine Learning
2. Big Data, Data Analytics and Python
- Why Python programming for Data Science
- Why R programming for Data Science
3. Data Wrangling and data Exploration
- Data Wrangling and Data Exploration
- Handle issues in Data Wrangling
- Model selection in Data Exploration
4. Introduction of Statistics
- How to approach the data Focus on data Assumption?
- EDA Technique (Quantitative and Graphical)
- Statistical Analysis
- Inferential method
- Descriptive Method
- Discussion on Mean, Median, Variance and Standard Deviation
- Understanding Skewness of Data using Bell Curve
5. Python Fundamentals
- Python Installation and Environment setup for Machine learning
- Python Basic programming
- Python Data types, statement and Loop
- Python Method and Class
- Python data Structure for Data Science implementation
6. Python Data Structures
- Tuples
- List
- Dictionary
- Set
- Sequence
7. Python Package for Scientific computing – Numpy
- NumPy – Introduction
- NumPy – Environment
- NumPy – Ndarray Object
- NumPy – Data Types
- NumPy – Array Attributes
- NumPy – Array Creation Routines
- NumPy – Array from Existing Data
- NumPy – Indexing & Slicing
- NumPy – Advanced Indexing
- NumPy – Broadcasting
- NumPy – Iterating Over Array
- NumPy – Array Manipulation
- NumPy – Binary Operators
- NumPy – String Functions
- NumPy – Mathematical Functions
- NumPy – Arithmetic Operations
8. Data Analysis using Pandas
- Using Pandas for Analyzing Data – Data Munging
- Using Pandas for Analyzing Data – Grouping and Aggregating
- Using Pandas for Analyzing Data – Visualization
- Analyzing Data with Pandas – Time Series
- Getting & Knowing Your Data
- Filtering & Sorting
- Grouping, Apply, Merge, Stats, Visualization
- Creating Series and Data Frames
- Time Series
- Deleting
9. Python package for Machine learning(Sklearn)
- Introduction to Scikit-learn package
- Datasets
- Estimators objects
- Matplotlib- Python Package for Graphical Representation of Dataset
- Example:Histogram, Plot,Box Plot,Line Graph,Heat Map
Supervised learning-Regression
- Introduction to Supervised learning
- Linear Regression-Model –How to evaluate a Linear Regression Model
- 7 Type of Regression Technique
- Multivariate regression Analysis
- Comparing Regression model with low anh High R squared Values
- Gradient Descent Algorithm and its Variants, Cost Function and choosing learning rate
10. Supervised learning and Supervised learning
- Introduction to Supervised learning-Understanding Problem, Loading Dataset, Data Wrangling, Model Selection, Train Dataset and Prediction
- Introduction to Unsupervised learning Understanding Problem, Loading Dataset, Data Wrangling, Model Selection, Train Dataset and Getting Insight of Dataset
11. Machine Learning Algorithm
All machine learning algorithm will be taught with theory background, brainstorming session and practical Data Set(1-2) for each algorithm
- Linear Regression(Housing Price and Diabetic Dataset)
- Gradient Descent of Machine Learning
- Logistic Regression (Titanic Dataset, Cancer Dataset)
- K Nearest Neighbours
- Support Vector Machines in Machine Learning- for classification and Regression problem
Improving SVM performance with Standard Scalar and MinMax Scalar
- Naïve bayes for continuous,Caterogiral and Text Data Classification
- Cost Function Evaluating model in machine Learning
- Unsupervised Learning -K Means Clustering in machine learning
- Discussion on Gradient Boosting for Regression and Classification problem
- Dimension Reduction and PCA algorithm –Unsupervised learning
- Decision Tree and Random Forest for classification and Regression problem
- Recommender System algorithm with Movie Dataset
12. Deep Learning using Tensorflow, Keras and Tflearn
- Brief introduction of Neural Network
- Different Activation Function for Neural network design
- NLP Fundamentals
- Bag of Words,Word2Vec,Tokenizer,TFIDFVectorizer and CountVectorizer method for Text Data Preparation for training using Deep learning
- Introduction to Tensor Flow- and Keras
- Employee Retention Dataset problem solution using Tensorflow and Keras
- Fraud Analytics Dataset solution using Deep learning
- CNN and RNN model for Deep learning
- How to solve image detection using CNN using million of image dataset
13. Project
-
- Introduction to Chatbot and A.I
- Use Case of Artificial Intelligence project with Chatbot, Speech to Text, NLP and Deep learning
- Solve Question answer Dataset training using RNN model with Text dataset
Data Science Training Duration & Pricing:
For Individuals
Duration: 1.5 Months and we also offer 2 Months Offline Support
Mode: Classroom & Online
Course Fees: Call us at +91-9900001329
For Corporate Training
The Mobignosis Corporate Training Program is designed for organisations who require practical upskilling for their employees to gain knowledge on the current trending technologies
Data Science
CERTIFICATION FOR DATA SCIENCE COURSE
Candidates receive Mobignosis course completion certificate upon successful completion of course
FAQs
The course is an instructor led classroom coaching session
The instructors are industry experts (Data Science Professionals) who consult with leaders in technology services like SAP, Capgemini, Cisco and many others
As a team of practicing Data Science professionals, we use the leading edge methodologies in our consulting work and have used the same methodologies to develop the Data Science course content for classroom coaching. So, you are exposed to the most up to date quality course contents
The Data Science Training program includes 2 months free technical support post training, the participants can repeat the session free of cost, For any additional assistance we are just a phone call away