Best institute for DATA SCIENCE training IN HYDERABAD
Introduction to Data Science
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- Why it is important and who are eligible
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Use Cases/Business Application (Retail, CPG, Banking, Telecom etc.)
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- Different scenario where DS can be applied to solve business problems
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Basics of Statistics –
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- Descriptive Statistics for
- Mean, Median, Mode, Quartile, Percentile, Inter-Quartile Range
- Standard Deviation, Variance
- Descriptive Statistics for two variables
- Z-Score
- Co-variance, Co-relation
- Chi-squared Analysis
- Hypothesis Testing
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Probability concepts –
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- Basic Probability, Conditional Probability
- Properties of Random Variables
- Expectations, Variance
- Entropy and cross-entropy
- Estimating probability of Random variable
- Understanding standard random processes
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Data Distributions
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- Normal Distribution
- Binomial Distribution
- Multinomial Distribution
- Bernoulli Distribution
- Probability, Prior probability, Posterior probability
- Naive Bayes Algorithm
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Basic Mathematics for Data Science
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- Limits,
- Derivatives, Partial Derivatives
- Gradients, Significance of Gradients
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Mastering Python/R Language
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- How to install python (Anaconda), sciKit Learn
- How to work with Jupyter Notebook and Spyder IDE
- Strings, Lists, Tuples, and Sets
- Dictionaries, Control Flows, Functions
- Formal/Positional/Keyword arguments
- Predefined functions (range, len, enumerates etc…)
- Data Frames
- Packages required for data Science in R/Python
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Introduction to NumPy
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- One-dimensional Array, Two-dimensional Array
- Pre-defined functions (arrange, reshape, zeros, ones, empty)
- Basic Matrix operations
- Scalar addition, subtraction, multiplication, division
- Matrix addition, subtraction, multiplication, division and transpose
- Slicing, Indexing, Looping
- Shape Manipulation, Stacking
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Introduction to Pandas
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- Series, DataFrame, GroupBy
- Crosstab, apply and map
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Data Preparation Techniques
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- Applications of PCA: Dimensionality Reduction
- Feature Engineering (FE)
- Combine Features
- Split Features
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Data visualization
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- Bar Chart
- Histogram
- Box whisker plot
- Line plot
- Scatter Plot and Heat Maps
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Machine Learning Algorithm – Data Preparation and Execution
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- Linear Regression
- Logistic Regression
- Optimization (Gradient Descent etc.)
- Decision Tree
- Random Forest
- Boosting and AdaBoost
- Clustering Algorithms (KNN and K-Means)
- Support Vector Machines
- Nave Bayes Algorithm
- Neural Networks
- Text Mining (NLTK)
- Introduction to Deep learning
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Note: All these Algorithms will be explained using one case study and executed in python.