**DataScience**

**About the course**

Data Science is a new field where students learn to analyze data in many forms to communicate insights. The course focuses on the introduction and basics of data science that can be used in various fields. In today’s, world, data is being collected in every industry and hence there exist ample opportunities for learners to apply for these jobs in reputed organizations.

**Who should learn this course?**

This course is for those who are seriously curious about data. The course is suited to IT professionals who wish to pursue a profession in the fields of economics, statistics or computer science. Students holding degrees in cloud computing can take up this course to add another level of aptitude to their qualification. Students with mathematics or statistics background are normally ones who are interested in this type of courses.

**What will you be learning?**

Within this course, you can get the opportunity to work inside a data science project. During each step, you will be learning different aspects of data science from data analyzing to its visualization and communication. Through the course, learners will be exposed to essential skills required to become a data scientist.

This course will survey and discuss the following topics of data science in detail:

- Data manipulation
- Working with big data
- Data analysis
- Statistics and machine learning
- Data communication
- Information visualization

Along with this, the learners will know the depth and breadth of data science usage in the modern world. From calculating network’s speed to storing the employer’s details, data is being used and stored everywhere and hence data science is a must requisite especially for of IT professionals.

**What are the pre-requisites for taking up this course?**

The ideal students who are prepared to take up this high level of course will have the following traits:

- He should have a background in statistics or mathematics
- He should have interest in data analysis and management
- He should have prior programming experience
- He should have an understanding of different variables like python data structures, function loops, dictionaries and lists etc.

Since this is a course that provides a degree level qualification, previous experience in computer science, descriptive statistics and programming is a must. In case you do not fulfill the pre-requisites you can always take up other relevant introductory courses provided by ABC learn.

### DataScience Classroom Timing

Jun Mon-Fri | 7:30 AM - 9:30 AM ( IST ) |
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#### DataScience Course Syllabus

- What is data science and why is it important?
- Pre-requisites
- Prospects
- Data science tools & technologies
- What is Machine Learning
- Why Machine Learning in Data Science
- Applications

- Types of data
- Raw data handling
- Processed or transformed data
- Decision making from data
- Statistics: Making sense of data

- Uni Variate analysis
- Measure of central tendency
- Mean–Median – Mode
- Range
- IQR
- Variance
- Standard deviation
- Correlation
- Probability Theory

- Uni Variate analysis
- Measure of central tendency
- Mean–Median – Mode
- Range
- IQR
- Variance
- Standard deviation
- Correlation
- Probability Theory

- Skewness
- Kurtosis
- Gaussian distribution
- Multivariate Gaussian distributions
- Binomial distributions
- Poisson distributions

- Supervised learning – regression, classification
- Unsupervised learning - clustering
- Reinforcement learning

- Why to learn python for data analysis? R vs Python.
- Installing python and Jupyter notebook
- Running simple programs in python
- Data types
- Variables
- Conditional statements
- Functions
- Loops
- Modules
- File I/O
- Exception handling

- Creating a Dictionary
- Accessing Values in Dictionary:
- Updating Dictionary
- Delete Dictionary Elements
- Properties of Dictionary Keys

- Creating a Tuples
- Accessing Values in Tuples:
- Updating Tuples
- Delete Tuple Elements
- Basic Tuples Operations

- Creating a list
- Accessing Values in Lists
- Updating Lists
- Delete List Elements

- Numpy array
- Array manipulation
- Array mathematics
- Array operations
- Vector and Matrix
- Broadcasting

- Introduction to series and data frames

- Pandas for Data Wrangling
- Overview
- Reading data
- Exploration
- GroupBy
- Indexing
- Hands on implementations

- Why missing value treatment is required?
- Why data has missing values?
- Methods to treat missing value
- Hands on implementations

- What is an outlier?
- What are the causes of outliers?
- What is the impact of outliers on dataset?
- Detecting outliers
- Treating outliers

- Linear regression
- Hypothesis
- Gradient Descent
- Prediction
- Normalization
- Hands on implementations
- Logistic regression
- Sigmoid function
- Decision Boundary
- Confusion matrix
- Hands on implementations

- What is feature engineering?
- What is the process of feature engineering?
- What is variable transformation?

- Mean Squared Error
- K fold cross validation
- Accuracy, Precision, Recall
- Hands on implementations

- Time Series variables
- Components of Time Series data
- Models for time series forecasting
- Exponential smoothing models
- Cross validation for time series data
- Hands on implementations

- Introduction to PCA
- PCA run with Unscaled and scaled predictors).
- Implement PCA
- Hands on implementations

- What is a decision tree?
- Decision tree algorithms
- How does it work?
- Implementation
- Hands on implementations

- What is random forest?
- Advantages of random forest
- Disadvantages of random forest
- Random forest implementation
- Hands on implementations

- What is KNN algorithm?
- How to select appropriate k value?
- Calculating distance
- KNN algorithm – pros and cons
- Hands on implementations

- Why clustering?
- K means clustering
- Number of clusters k=?
- Hierarchical Clustering
- DBSCAN Clustering
- Performance evaluation
- Pros and cons
- Hands on implementations

- Overview
- Classification Using a Separating Hyperplane
- The Maximal Margin Classifier ?Non-separable Case
- Support Vector Classifiers - Details
- Support Vector Machines - Classification with non-linear boundaries
- Hands on implementations

- Introductory Concepts
- Feed Forward Neural Networks
- Multilayer Feed Forward Neural Networks
- Motivation and formulation
- Learning Algorithm
- Backpropagation
- Convergence and Optimization
- Loss functions
- Limitations
- Applications

- Intro to Natural language processing and sentiment analysis
- Python NLTK library
- Bag of words concept
- Text Preprocessing
- Noise Removal
- Lexicon Normalization
- Lemmatization
- Stemming
- Stop words removal
- Statistical features
- TF – IDF
- Frequency / Density Features
- Text analysis with real life datasets
- Hands on implementations

- How to create a scatter plot?
- How to create a histogram?
- How to create a bar chart?
- How to create a stacked bar chart?
- How to create a box plot?
- How to create an area chart?
- How to create a heat map?
- How to plot a geographical map?
- Hands on implementations

- Choose your own algorithm
- Final Project
- Further study
- Next steps in your career

- Linear regression
- Hypothesis
- Gradient Descent
- Prediction
- Normalization
- Hands on implementations
- Logistic regression
- Sigmoid function
- Decision Boundary
- Confusion matrix
- Hands on implementations

### Learn DataScience With The Best Institute In Hyderabad

#### Real Live Projects

Learn use cases of software industry and companies with our live projects. Become expert by learning analytical tools like R, SAS, Hadoop, Python, Tableau etc.

#### Experienced Trainers

Practical implementation is now the new bench mark and feel it with our constant hands on live projects and training.

#### Placement Assistance

Our great career counseling team is always there to help you out in finding the best career for you. Availability of applications, projects and case studies to make you an expert of the industry.