### Data Science Course

##### About Data Science Course in Hyderabad

Data Science Course in Hyderabad designed for students and professionals who want to build their career as a Data Analyst or Data Scientist. Data Science is one of the trending course in the present day. There a massive demand for Data Analyst or Data Scientist. Every company may be a start-up or Mnc’s looking for a Data Analyzer.

Data Analyst must be flexible with Coding in Python or R-language. Statistical and mathematical problem solver with data analyzing skills will be an added advantage.

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##### Data Science Course in Hyderabad Curriculum

**Machine Learning and its Benefits**

**Lesson 1 – **Python Basics**Lesson 2 –** Python Data Structures**Lesson 3 –** Python Programming Fundamentals**Lesson 4 – **Working with Data in Python**Lesson 5 –** Working with NumPy Arrays

**Data Science Course with Python**

**Key Learning Objectives of Data Science Course in Hyderabad**

- Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
- Install the required Python environment and other auxiliary tools and libraries.
- Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions.
- Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions.
- Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
- Perform data analysis and manipulation using data structures and tools provided in the Pandas package.
- Gain expertise in Machine Learning using the Scikit-Learn package.
- Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline.
- Use the Scikit-Learn package for natural language processing.
- Use the matplotlib library of Python for data visualization.
- Extract useful data from websites by performing web scraping using Python.

**Data Science Course curriculum**

**Lesson 1 – **Data Science Overview.**Lesson 2 –** Data Analytics Overview.**Lesson 3 –** Statistical Analysis and Business Applications.**Lesson 4 –** Python Environment Setup and Essentials.**Lesson 5 –** Mathematical Computing with Python (NumPy).**Lesson 6 –** Scientific Computing with Python (Scipy).**Lesson 7 –**Data Manipulation with Pandas.**Lesson 8 –** Machine Learning with Scikit–Learn.**Lesson 9 –** Natural Language Processing with Scikit Learn.**Lesson 10 –** Data Visualization in Python using Matplotlib.**Lesson 11 –** Web Scraping with BeautifulSoup.

- What is Deep Learning?
- What is Data?
- Data Preprocessing and its implementations.
- Exploratory Data Analysis.
- Feature Engineering and Extraction.
- Data Wrangling.
- Data Manipulation.
- Data Visualization.
- Statistics.
- Linear Algebra.
- Calculus
**Data Science Course in Hyderabad- Machine Learning**- Supervised Problems
- Unsupervised Problems
- Semi-Supervised Problem
- Supervised Algorithms

**Linear Regression**

- Correlation Analysis
- Principles of Regression
- Introduction to Simple Linear Regression
- Python Flask
- Introduction to Python Flask (deployment)

- Multiple Linear Regression
**Description:**Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

- Scatter diagram
- Correlation Analysis
- Correlation coefficient

- Ordinary least squares
- Principles of regression
- Splitting the data into training, validation and testing datasets
- Understanding Overfitting (Variance) vs Underfitting (Bias)
- Generalization error and Regularization techniques
- Introduction to Simple Linear Regression
- Heteroscedasticity / Equal Variance

**Description:** Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

**Topics**

- LINE Assumption
- Collinearity (Variance Inflation Factor)
- Linearity
- Normality

- Multiple Linear Regression
- Model Quality metrics
- Deletion diagnostics

**Logistic Regression**

**Description:** Learn to analyze Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

**Topics**

- Principles of Logistic Regression
- Types of Logistic Regression
- Assumption and Steps in Logistic Regression
- Analysis of Simple Logistic Regression result

**Description:** Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures, and its interpretation. Know what is a confusion matrix and its elements. Get introduced to the “Cut off value” estimation using the ROC curve. Work with a gain chart and lift chart.

**Topics**

- Multiple Logistic Regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1

- Receiver operating characteristics curve (ROC curve)
- Lift charts and Gain charts

**Description:** Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples, and Random Forest algorithms.

**Topics**

- Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
- Greedy algorithm
- Measure of Entropy
- Attribute selection using Information Gain
- Ensemble techniques
- Decision Tree C5.0 and understanding various arguments
- Random Forest and understanding various arguments

- Support Vector Machine
- Naïve Bayes
- Bagging and Boosting Algorithms
- Unsupervised Algorithms
- Clustering
- K-Means Clustering
- Prototype based Clustering
- DBSCAN
- RDBSCAN
- Association
- Apriori Algorithm

**Data Science Course in Hyderabad – Deep Learning**

- Neural Network its intuition and implementation
- Single Layer Perceptron intuition
- Multi-Layer Perceptron intuition and differences
- Knowledge on ANN
- Knowledge on CNN and its Implementation
- RNN Intuition
- LSTM Intuition
- GRU Intuition
- Knowledge on GANS
- Knowledge on SAIMESE Net
- Working with Keras
- Knowledge of how TensorFlow works.

**Data Science Course in Hyderabad- Natural Language Processing:**

- Text reading in PYTHON
- Text preprocessing and its techniques
- Text Mining with Python
- Text Extraction uring NLTK
- Text Summarization
- Extractive summarization
- Abstractive summarization

- Sentiment Analysis
- Spacy
- Regular Expressions
- Feature representations like Bag of Words, TF-IDF, Word2Vec.
- Knowledge on Glove(Global Vectors)
- Stop-words
- N-Grams
- Word-Net
- Grammarly-Bot
- Text Classification

**Data Science Course in Hyderabad- Computer Vision:**

- Image preprocessing
- Image segmentation
- Binarize Images
- Blurring Images
- Cropping Images
- Detect Edges
- Enhance Contrast of Color Image
- Enhance Contrast of Greyscale Image
- Harris Corner Detector
- Installing OpenCV
- Isolate Colors
- Load Images
- Remove Backgrounds
- Save Images
- Sharpen Images
- Shi-Tomasi Corner Detector
- Using Mean Color as A Feature

**Data Science Course in Hyderabad Projects:**

- Santander Bank Customer Satisfaction
- Digit Recognition
- Author Identification
- Cancer Detection

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