What Exactly Is Data Science?
Data Science is an interdisciplinary field that works deals with the process of interpreting large sets of Big Data to extract actionable insights from large sets of Big Data. In other words, Data Science can be interpreted as the study information which is generated from a variety of data sources (structured and unstructured), and how this information can be turned into a valuable resource that can fuel the Business and IT strategies. Data Science is very crucial for business development as it helps enterprises in leveraging the power of Big Data to its full potential and helps them make strategically accurate business decisions. The diverse techniques in Data Science can help businesses to manage their rein in costs, increase efficiencies, recognize new market opportunities thereby; it helps them gain a competitive advantage over their competitors.
As the demand for Data Science is growing beyond leaps and bounds it has become a lucrative career option for people coming from different backgrounds. This Data Science Training in Hyderabad program lets you stay on the right career path to achieve success in a career in Data Science. This Data Science course is the perfect platform to master the skills in the core technical and non-technical areas that are associated with Data Science.
Why Should You Learn Data Science?
For post-COVID business recovery, enterprises across the industry verticals have started relying on Data Science at large. Both Big Data and Data Science have become the driving force for enterprises to explore unforeseen revenue opportunities. If we look at the stats from the last ten years, we can notice an unprecedented growth in the demand for Data Science. This stupendous growth in the demand for Data Science isn’t limited to any one particular sector and spreads across the verticals like IT, Healthcare, Business, Banking, Telecom, Insurance, Travel, Education, Entertainment, Defence, Government, and several other sectors. Amid the growing demand for Data Science, Data Scientists are being recruited at large by enterprises across the industry verticals.
Data Science Course in Hyderabadprogram equips you with skills in both theoretical and technical aspects of Data Science. By making you handle multiple capstone projects, assignments, and case studies, this course makes you a fully competent Data Scientist.
Course Details
Course Objectives of Data Science Training in Hyderabad Program
Data Science Course in Hyderabad program follows a well-drafted curriculum that covers the complete breadth of the concepts in Data Science. As a part of this Data Science course, students will get to leverage skills both theoretical & practical wise by working on multiple capstone projects & case studies. This Data Science course training is mainly aimed at helping students develop skills in Data Science concepts like
Objectives of this Data Science training program is to help the students
Modules of Data Science Training in Hyderabad
This Data Science course in Hyderabad program follows a structured curriculum that is specially designed by the expert Data Scientists. We frequently revise and update the content of this curriculum to make it relevant with the current industry standards and it covers all the latest updates in this field. At the foundation level, the curriculum talks about Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis and this will be followed by topics like Decision Tress, Random Forest, Clustering Techniques, Probability Distribution, Hypothesis Testing, Supervised and Unsupervised Learning, Linear Regressions. In the next part of the course, students will be learning core technical topics like Data Mining Data Modeling, Algorithm Training, Data Visualization, Predictive Modeling, Recommendation Engines, Machine Learning, Text Mining, and Natural Language Processing and further advanced topics. Post this Data Science training completion, students will become fully competent and job-ready specialists in Data Science.
- What is data science?
- How is data science different from BI and Reporting?
- What is difference between AI, Data Science, Machine Learning, Deep Learning
- Job Land scape and Preparation Time
- Who are data scientists?
- What skillsets are required?
- What is day to day job of Data Scientist
- What kind of projects they work on?
- End to End Data Science Project Life Cycle
- Data Science roles – functions, pay across domains, experience
- Data types
- Continuous variables
- Ordinal Variables
- Categorical variables
- Time Series
- Miscellaneous
- Common Data Science Terminology
- Descriptive statistics
- Basics concepts of probability
- Frequentist versus Bayesian Probability
- Axioms of probability theory
- Permutations and combinations
- Conditional and marginal probability
- Joint Probability
- Bayes Theorem
- Probability Mass Function and Probability Density Function
- Cumulative Mass Function and Cumulative Density Function
- Central Tendencies
- Mean
- Median
- Mode
- Spread
- Variance
- Standard Deviation
- Effects on central tendencies after transformations
- Quartile Analysis
- Implementation of central tendencies using python
- Box Plots for outlier identification
- Drawing Box plots using python
- Sampling
- Need for Sampling?
- Different types of Sampling
- Simple random sampling
- Systematic sampling
- Stratified Sampling
- Implementation of sampling techniques using python
- Data distributions
- Normal Distribution
- Binomial Distribution
- Binomial Approximated to Normal
- Implementation of distributions using python
- Inferential statistics
- Why inferential statistics?
- Z score calculation
- Defining p value and implementations using python
- Inferring from sample to population
- Sampling distribution of sample means
- Hypothesis testing
- Confidence Interval
- Testing the hypothesis
- Type I error
- Type II error
- Null and alternate hypothesis
- Reject or acceptance criterion
- A Primer to R programming
- What is R? Similarities to OOP and SQL
- Types of objects in R – lists, matrices, arrays, data.frames etc.
- Creating new variables or updating existing variables
- If statements and conditional loops - For, while etc.
- String manipulations
- Sub setting data from matrices and data.frames
- Casting and melting data to long and wide format
- Merging datasets
- Understanding the reason of Python’s popularity
- Basics of Python: Operations, loops, functions, dictionaries
- Numpy – creating arrays, reading, writing, manipulation techniques
- Ground-up for Deep-Learning
- Getting to understand structure of Matplotlib
- Configuring grid, ticks.
- text, color map, markers, widths with Matplotlib
- configuring axes, grid,
- hist, scatterplots
- bar charts
- multiple plots
- 3D plots
- Correlation matrix plotting
- Introduction to pandas
- Data loading with Pandas
- Data types with python
- Descriptive Statistics with Pandas
- Quartile analysis with Pandas
- Sort, Merge, join with Pandas
- Indexing and Slicing with pandas
- Pivot table, Aggregate and cross tab with pandas
- Apply function for parallel processing with Python
- Cleaning Data with python
- Determining correlation
- Handling missing values
- Plotting with Pandas
- Time series with Pandas
- Dealing Prediction problem
- Forecasting for industry
- Optimization in logistics
- Segmentation in customer analytics
- Supervised learning
- Unsupervised Learning
- Optimization
- Types of AI : Statistical Modelling, Machine Learning, Deep Learning, Optimization, Natural Language Processing, Computer vision, Speech Processing, Robotics
- Linear Regression
- Assumptions
- Model development and interpretation
- Sum of least squares
- Model validation – tests to validate assumptions
- Multiple linear regression
- Disadvantages of linear models
- Logistic Regression
- Need for logistic regression
- Logit link function
- Maximum likelihood estimation
- Model development and interpretation
- Confusion Matrix – error measurement
- ROC curve
- Measuring sensitivity and specificity
- Advantages and disadvantages of logistic regression models
- Time series analysis - Forecasting
- Simple moving averages
- Exponential smoothing
- Time series decomposition
- ARIMA
- Model validation and deployment
- RMSE – Root Mean squared error
- MAPE – Mean Average Percentage Error
- Confusion matrix and Misclassification rate
- Area under the curve (AUC) , ROC curve
- Supervised Learning
- Decision trees and Random Forest
- C5.0
- Classification and Regression trees(CART)
- Process of tree building
- Entropy and Gini Index
- Problem of over fitting
- Pruning a tree back
- Trees for Prediction (Linear) – example
- Tress for classification models – example
- Advantages of tree-based models?
- Association Rule Mining
- Rules generation from decision trees,
- Apriori algorithm
- Support, confidence and lift measures
- Support Vector Machines
- Linear learning machines
- SVM case for linearly separable data
- Kernel space
- Neural Networks
- Motivation for Neural Networks
- Perceptron and Single Layer Neural Network
- Back Propagation algorithm
- Feed Forward Neural Net
- Sigmoid parameters
- Weights initialization
- Decay of weights
- Learning rate
- Momentum
- Ensemble Techniques
- Bagging
- Boosting
- Stacking
- Gradient Boosting Machines
- Unsupervised Learning
- Clustering Techniques
- Hierarchical clustering
- K-Means clustering
- Distance measures
- Applications of cluster analysis – Customer Segmentation
- Collaborative Filtering, PCA
- NLP I - Text Preprocessing
- Tokenization
- Stemming
- Lemmatization
- NLP II – Text Modelling
- POS tagging
- TFIDF and classification
- ReLU
- Sigmoid, Depth vs Width tradeoffs
- Convolutional networks
- Concepts of filters
- Sliding
- Pooling and Padding
- Comparison between DL and ML performances over the MNIST dataset
- Business problem to an analytical problem
- Guidelines in model development
- Big data and analytics?
- Leverage Big data platforms for Data Science
- Introduction to evolving tools
- Machine learning with Spark
- Creation of R-Server clusters
- Computation of Big-Data ML algorithms over the Azure cloud
- Why is it important for Data-Analyst
- Tableau workbook walkthrough
- Instruction of creation of your own workbooks
- Demo of few more workbooks
Tools Covered
