About Course
This Data Science Online Training session will help the aspirants to survey in each and every topic from the basic level to face complex problems in an easy manner. Industry Expertise delivers classes with real-time examples with the main focus on breadth and in-depth skills. Individuals can become a Data scientists by getting project experience and can easily stay updated in a career with lifetime assessments to live classes. High Interactive classes with hands-on training experience are provided to the aspirants to remote Data Science Labs.
HappieLabs has come up with the main aim to provide a huge In-Depth subject knowledge skill set as per the current IT Industry and learn by working with an end to end Data Science projects approved by Expertise. Globally Recognized Certification is provided to aspirants after the completion of Online Data Science Training. Project Oriented Training sessions are provided to the aspirants to acquire practical knowledge skill sets. Mock Interview sessions with career guidance are ensured regarding hiring companies to get placed in MNCs.
What are the vacancies in Data Science?
The employment opportunities in data science are many. Graduates and professionals interested in a career in this field can take Data Science Online Course. After receiving the certification, aspirants can apply for different job profiles depending on their specialist knowledge, experience, and professional goals. The different job profiles in this area are:
- Analytics Manager
- Data Analyst
- Data Scientist
- Data Architect
- Research Scientist
- Data Statistician
Why we have to take data science course at Happie Labs?
Happie Labs offers the best Data Science Training, led by industry experts with more than a decade of experience in the field. Happie Labs follows a project-based learning methodology to make it easier for students to learn hands-on. In addition to lectures, the online training courses also include discussions, quizzes, and other activities.
Course Curriculum:
- INTRODUCTION TO DATA SCIENCE
- Need of Data Science
- What is Data Science?
- Analogy of Data Science with examples
- Real world examples
- How Facebook is using AI
- How Netflix uses AI
- LIFE CYCLE OF DATA SCIENCE
- Business Requirement
- Data Collection
- Data Processing
- Exploratory Data Analysis (EDA)
- Modelling
- Model Validation
- Optimization
- Deployment
- Life Cycle examples in Telecom Industry
- PYTHON & LIBRARIES
- What is Python?
- History of Python
- Versions of Python
- Keywords and Identifiers
- Statements and Comments
- Variables
- Data types (Numbers, List, Tuples, String, Sets & Dictionary)
- Type conversions
- Python input/output
- Python operators
- Python namespaces
- Python flow control (If, if else, nested if, nested if else, break, continue, pass)
- Python looping statements (For, While)
- Python Functions
- Python Recursions
- Python Anonymous functions
- Global local, non-local
- Python Modules
- Python Packages and Libraries (Numpy, Pandas, Matplotlib, Seaborn, Sklearn)
- Python file systems
- Python Exceptions and Exceptions handling
- Python date and time functions
- Python regex
- Python OOPs
- STATISTICS
- Basics Statistics
- Descriptive statistics and inferential statistics Measure of central tendency -Mean, Median and Mode
- Measure of Dispersion-Range, Variance, standard deviation and coefficient of variation
- Variance
- Quartiles
- Percentiles
- Frequency distribution
- Box Plots
- Missing values and Outliers
- Sampling techniques
- Introduction to Probability
- Bi-nominal distribution
- Poisson distribution
- Normal distribution
- Hypothesis testing
- T-student distribution
- Chi-square distribution
- ANOVA
- Vectors, Linear Algebra, Matrix and derivatives
- DATA HANDLING & DATA MANIPULATION USING PANDAS
- Data importing
- Working with datasets
- Manipulating the data sets
- Subset the data
- Sort the data
- Creating new variables
- Bins creation
- Identifying & removing duplicates
- Exporting the datasets into external files
- Data Merging
- Pivot table analysis
- Data visualization
- Matplotlib
- Seaborn
- Heatmap
- Worldmap
- Histogram
- Bar Chart
- Pie Chart
- Scatter Matrix Pandas
- Plots
- Line Graphs
- Artificial Intelligence (Machine Learning & Deep Learning)
SUPERVISED LEARNING -REGRESSION
- Linear Regression
- Multiple linear Regression
- Rigid Regression
- Lasso Regression
- Elastic Net Regression
- Polynomial Regression
- Gradient Decent
- KNN-Regressors
- Decision Tree Regressors
- Random Forest -Regressor
- Support vectors-Regressors
SUPERVISED LEARNING -CLASSIFICATION
- Logistic Regression
- Decision Tree Classifier
- Naive Bayes
- KNN-Classifiers- Binary labels and multi labels Support Vector Machines
- Support vectors-Classifiers
- Ensemble learning
- Bagging
- Boosting
- Random Forest-Classifier
UN-SUPERVISED LEARNING
- Clustering Analysis
- Hierarchical Clustering
- Clustering K –Means
- Clustering K –Modes
- Recommendation engine
MODEL SELECTION AND CROSS VALIDATION
- How to validate a model?
- What is a best model?
- Types of data
- Types of errors
- The problem of over fitting
- The problem of under fitting
- Bias Variance Tradeoff
- Cross Validation
- Boot Strapping
- Tensor flow and Keras
- Introduction to Tensor flow and Keras
- Constant
- Place holders
- Variables
- Neural Networks (ANN, CNN and RNN)
- Neural Networks Introduction
- Neural Network Intuition
- Neural Network and vocabulary
- Neural Network algorithm
- Math behind Neural Network algorithm
- Building the Neural Networks
- Validating the Neural network model
- Neural Network applications
- Image recognition using Neural Networks
- Multi layers Neural Networks (ANN)
- Neurons
- Weights
- Activations
- Networks of Neurons
- Training Networks
- Back propagation
- Gradient Descent
- CNN
- Feature learning
- Convolution
- Pooling
- Classification learning
- Flatten
- Fully Connected
- SoftMax
- NLP (Sentimental Analysis, TF-IDT, Bag of words, Word2Vec, chat bot analysis)
- Introduction to natural language processing
- First step of text processing
- Named entities from text using NLTK and Spacy
- Social media data extraction
- Feature engineering from text
- Topic Modeling
- Master of articulating text
- Bag of words, TF-IDF, Word2Vec
- NLP Sentimental analysis
- Introduction to fast text and text hero
- NLP with deep learning
