Data Science Summer Camp Program for Students 2020
Ever imagined if you could grasp some great opportunity so after completion of your B.tech/B.E or MCA semester every year?
Well, Innomatics Research Labs is here again with a great summer camp ahead of this year.
Making sure that your summer doesn’t get boring, we are here to enhance your summer holidays thereby offering you certified training in Data Science course at Hyderabad.
Data Science is one of the top leading courses in the field of information technology. Most of the graduates and working professionals are running after this course, in order to get certified with reputed training institutes like Innomatics Research Labs, Hyderabad. Every year in mid of spring, we take up an initiation to cater students with professional IT courses so that, every student who goes for a job in future, has an outstanding resume with a certified specialist in the particular course.
And this year, we have chosen Data science course as a part of summer camp training program 2020. The indefinite demand for this course at Innomatics Research Labs has led us towards organizing a very well-planned workshop for students in data science.
Summer camp for Bachelors & Masters
The data science summer camp training program is a systematically scheduled 6 weeks intensive training program for the undergraduate students pursuing Bachelors and Masters. The course has intensified syllabus for the students which initializes from basics of data science and ends up with mastering one in the vast field of data science.
The course is designed in such a way that every trainee gets to the niche knowledge over the concepts of data science and also gains a work experience for the virtual period of time at Innomatics, Hyderabad. Also, the course content is so easy to understand and is also unique in such a way that the content doesn’t match any other data science training institute in Hyderabad.
Summer Camp 2020 Program Highlights
6 weeks of intensive training program at the training premises.
Opportunity of getting trained from top academicians and industrialists.
Personal Training assistance from certified mentors.
Globally accepted certification after the completion of the course.
Great opportunity of gaining extra skills from your fellow peers.
Enhances your resume thereby providing a higher level of opportunities.
Guaranteed placement assistance.
Be Luckiest to be hired as interns basing upon your performance at training.
What would be the Program Outcome?
An e-material and certification will be provided to every trainee
Students will be able to gain real time industry knowledge
Hands-on experience with live projects
The trainees will be able to gain a thorough knowledge over aspects of data science
Candidates will be further eligible for advanced data science courses
Candidates will gain access over machine learning and big data analytics concepts
Opportunity to work and create Chat BOTs and 30+ use cases
Industry connect with the experts across various technologies and domains
Reverse presentations to enhance your presentation skills and overcome the stage fear
Viva to test and provide the feedback
Data Science Curriculum (Summer camp program)
1. Introduction to Data Science
- Introduction to data science
- Application of Data Science
- Life cycle of data science projects
- Data and its various forms
2. Python - Core, Advanced
- 1. INTRODUCTION
- What is Python?
- Why Data Science requires Python?
- Installation of Anaconda
- Understanding Jupyter Notebook
- Basic commands in Jupyter Notebook
- Understanding Python Syntax
2. Data Types and Data Structures
3. Control Flow and Conditional Statements
- Conditional Operators, Arithmetic Operators and Logical Operators
- If, Elif and Else Statements
- While Loops
- For Loops
- Nested Loops
- List and Dictionary Comprehensions
- Code Optimization
- Lambda Functions
- Modules and Packages
5. File Handling
- Create, Read, Write files
- Operations in File Handling
- Errors and Exception Handling
6. Python Datatime
7. Object Oriented Programming(OOP)
- Attributes and Class Keywords
- Constructers and Destructors
- Using Self Parameter
- Class Object attributes and Methods
- Overloading and Over Riding
- Method Overloading
- Operator Overloading
- Super Keyword
- Method Overriding
3. Python for Data Analysis & Visualization
- 8. Numpy
- Basic Operations in Numpy
- Array Processing
- Data Frames
- Indexing and slicing
- Merging Joining
- Missing Values
- Data Input and Output
- Cross tab
10. Data Visualization
- Introduction to Matplotlib
- Line plots
- Box and Violin Plots
- Visualization with Seaborn
4. Understanding Text using Python
- 10. Regular Expressions
- Literals and Meta Characters
- How to Regular Expressions using Pandas?
- Inbuilt Methods
- Pattern Matching
- Data Mining
This project starts completely from scratch which involves collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of Data Science Life Cycle which involves
- Data Collection
- Data Mining
- Data Preprocessing
- Data Visualization.
Ex: Text, CSV, TSV, Excel Files, Matrices, Images
5. Statistics - DESCRIPTIVE & INFERENTIAL
- Basic Statistics Terminology
- What is Statistics?
- How Statistics is used in Data Science
- What is Probability?
- Population and Sample
- Sampling Techniques
- Convenience Sampling
- Simple Random Sampling
- Systematic Random Sampling
- Stratified Sampling
- Cluster Sampling
- Dependent and Independent Variables
- Qualitative and Quantitative Data
- Categorical Data
- Numerical Data
- Discrete and Continuous Data
- Categorical Data
- Mean, Median and Mode
- Standard Deviation and Variance
- Box Plot and Distribution
Basics of Probability
- Probability vs Statistics
- Probability Rules
- Probability Types
- Marginal Probability
- Joint Probability
- Union Probability
- Conditional Probability
- Conditional Probability
- Bayes Theorem
- Confusion Matrix
- Variance of Distribution
- Discrete Probability Distribution
- Continuous Probability Distribution
- Normal Distribution
- Gaussian Distribution
- Confidence Interval
- Standard Error
- Margin of Error
- Hypothesis Testing
- Chi-square test
6. MACHINE LEARNING – SUPERVISED LEARNING
- What is Machine Learning?
- Difference between Supervised Learning and Unsupervised Learning?
- Difference between Regression and Classification Models?
2. Linear and Multiple Regression
- Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction.
- Hands on Linear and Multiple Regression using a use case.
- Understanding the summary output of Linear Regression
- Residual Analysis
- Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
- Hypothesis testing of Regression Model
- Confidence intervals of Slope
- R-square and goodness of fit
- Influential Observation – Leverage
- Polynomial Regression
- Categorical Variable in Regression
3. Logistic Regression
- Logistic Regression Intuition
- Understanding Logit Function.
- Hands-on Python Session on Logistic Regression using business case.
- Measuring the Evaluation Metrics – Confusion Metrics, Accuracy, Precision, recall and ROC Curve.
4. Navie Bayes Classifier
- Review probability distributions, Joint and conditional probabilities
- Model Assumptions, Probability estimation
- Required data processing
- Feature Selection
5. Principal Compound Analysis (PCA)
- Introduction to dimensionality reduction and it’s necessity
- Background: Eigen values, Eigen vectors, Orthogonality
- Principal components analysis (PCA)
- Feature Extraction
- Advantage and application of Dimensionality reduction.
6. Decision Tree (Rule – Based)
- Decision nodes and leaf nodes
- Variable Selection, Parent and child nodes branching
- Stopping Criterion
- Tree pruning and Depth of a tree
- Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
- Regression using decision tree
- Interpretation of a decision tree using If-else
- Pros and cons of a decision tree
- Accuracy estimation using cross-validation
7. K-Nearest Neighbor (Distance Based Learning)
- What is KNN and why do we use it?
- KNN-algorithm and regression
- Curse of dimensionality and brief introduction to dimension reduction
- KNN-outlier treatment and anomaly detection
- Pros and cons of KNN
8. Support Vector Machine (Distance Based Learning)
- Linear learning machines and Kernel space, making kernels and working in feature space
- Hands on example of SVM classification and regression problems using a business case in Python.
9. Esemble Methods
- Introduction to Ensemble
- Bias and Tradeoff
- Bagging & boosting and its impact on bias and variance
- Random forest
- Gradient Boosting
- Predictive Analytics
- Banking Use cases – Customer Service prediction,
- Health care Use cases – Heart Disease, Diabetics
- Insurance Use cases
- Telecom Churn Prediction
- Bike Sharing
- Air Quality
7. MACHINE LEARNING – UNSUPERVISED LEARNING
- Different clustering methods
- review of several distance measures
- Iterative distance-based clustering
- Dealing with continuous, categorical values in K-Means
- Constructing a hierarchical cluster, and density-based clustering.
- Test for stability check of clusters
- Hands-on implementation of each of these methods in Python
- Air Quality
8. Natural Language Processing (NLP) – Text Mining
- . INTRODUCTION
- What is Text Mining?
- Structured and Unstructured Data
- Extracting Unstructured text from files and websites
2. Text Preprocessing
- Regular Expressions for Pattern Matching
- Text Normalization
- Text Tokenization
- Sentence Tokenization
- Word Tokenization
- Text Segmentation
3. Natural Language Understanding (NLP Statistical)
- Automatic Tagging
- N-grams Tagging
- Transformation based Tagging
- Bag of Words
- POS Tagging
- TF – IDF
- Cosine Similarity
- Thinking about the math behind text; Properties of words; Vector Space Model
- Named Entity Recognition
- Relation Extraction
4. Text Classification
- Text Mining
- Sentiment Analysis
- Spam Detection
- Dialogue Prediction
9. Artificial Intelligence
- 1. Introduction to Neural Networks
- Introduction to Neural Network
- Introduction to Perceptron
- Activation Functions
- Cost Functions
- Gradient Decent
- Stochastic Gradient Descent
- Back propagation
2. Deep Frameworks
- Installing Tensorflow and Keras
- Tensorflow and Keras Basic Syntax
- Tensorflow Graphs
- Variables and Placeholder
- Saving and Restoring Models
3. Artificial Neural Network with Tensorflow
- Neural Network for Regression
- Neural Network for Classification
- Evaluating the ANN
- Improving and tuning the ANN
4. Convolution Neural Networks
- Convolution Operation
- ReLU Layer
- Full Connection
- Softmax and Cross Entropy
- Image Classification
5. Keras (Backend Tensorflow)
- Keras vs Tensorflow
- Introduction to Keras
- Building Artificial Neural Network with Keras
- Building Convolution Neural Network with Keras
- Face Recognition
Face Recognition project gives details of the person and can recognize the gender and names. This project involves in
- Collection of images
- Preprocessing the data
- Applying the Model (Machine Learning or Deep Learning)
- Training and Testing using the model
- 1. Introduction to Neural Networks
Ex: Security Unlock, Gender Recognition, Identity Recognition
Virtual Assistants are now a common requirement for an Organization. But, to make the assistant more effective we are now into the chatbots which involve Natural Language Process, Deep Learning and Artificial Intelligence. These interactive chatbots are designed to serve as an intellectual responsive process.
Ex: Alexa, Siri, Google Assistant
- Creating pickle and frozen files
- Cloud Deploying Machine Learning and Deep Learning model for production
A few reviews from our students
Frequently Asked Questions (FAQs) on Summer Camp program
What is the summer workshop about?
Every year we organize training for students in professional courses in the field of information technology in order to make their springs more interesting. This course would help students to gain knowledge over the chosen course and further turn them as working Professionals, Freelancers in corporate institutions.
Is there any chance of switching this workshop to online mode?
No, as this is a part of our summer training program, we don’t have the option of online mode. However, other than the summer workshop you will have an opportunity to go online for various courses.
Will this help me gaining a job after completion of my graduation in B.tech?
Yes. You will have a great opportunity ahead to work in various field of data science at reputed companies globally with handsome salary packages.
Who is the faculty involved in training?
We have 20+ Data science industry professionals from different fortune 500 companies across multiple domains. We also have a dedicated in-house data scientist team to help you with the queries and concerns if you have any.
I am a non IT Graduate/Professional. Can I learn data science?
Yes… We have curated this curriculum in such a way that even a NON IT person also can understand and apply the knowledge in data science field.
What are the skills required to learn this course?
No special skills required, as basic knowledge over computer & internet tools is more than enough. However, basics over certain programming languages would be beneficial, but isn’t an essential one.
Is it worth learning Data science in summer camp program?
Yes. There are 90,000 jobs available in India in data science field. Artificial intelligence is the future. This course helps you in getting a good knowledge in Python, machine learning, deep learning and other tools.