Data Science Training 2019-05-22T12:23:25+05:30

Data Science Training

  • 60 Days Online Training
  • 60+ Days Classroom Training
  • Live Projects Training

Using multiple disciplines of programming, scientific methods and algorithms to process data in varied forms to extract meaningful information and insight is the primary object of Data Science.  A Data Scientist is a person who uses multiple disciplines of programming and complex algorithms to derive creative information out of large data volumes.

Online Training

Rs. 25,000 *100 days access


Classroom Training

Rs. 25,000 *100 days access

100% Placement Assistance
  • Best Discount
  • Expert Educators
  • Flexible Schedule
  • 24x7 Tech Support
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Upcoming Batches

Date

Name

Time

Duration

Branch

Download

Enroll

April 22

DataScience

9:00 pm – 10:00 pm IST

120 days

Ammerpet

Can’t find convenient schedule? Let us know

Training Features

Top Industry Trainers

All our trainers are real-time industry experts. Quality of training is our primary motto and we ensure each and every program of ours are delivered by the best trainers.

Industry Relevant Curriculum

Course designed keeping in mind the present and future needs of the Industry. All our training programs are constantly updated and tuned to meet Industry requirements.

Real-Time Case Studies

Real-Time case studies and project are mandatory part of our training programs. All the assignments are designed to help students understand practical applications of the learning’s.

Flexible Schedule

With options to join classroom and online batches, you have a wide array of options in terms of batches, timing and duration allowing to you plan your learning, and achieve your carrier goals.

Feedback Management

Continuous feedback and interaction with our student community help us identify concern area and mitigate issue early on ensuring a great learning environment.

State-of-art Lab Infrastructure

Best in class Lab infrastructure to help students work on the latest assignments and project. Practical application of the learning ensures a more satisfied training.

Description

Using multiple disciplines of programming, scientific methods and algorithms to process data in varied forms to extract meaningful information and insight. Converting large volumes of data in creative ways to generate business value is the primary objective of a data scientist. With the emergence of data mining and analytics playing key role in decision making process in organizations big and small, Data science has emerged as a key technology skill area.
Quality Thought offers the best Data science Training program focusing on various aspects and dimension of learning. As data science is a multi-disciplinary module, Quality Thought has put extra focus to ensure our data science curriculum covers all the industry relevant topics and subject. Our course curriculum has been prepared by trainers with real-time industry experience with a lot of emphasis on real-time case studies and assignments. The primary objective of the data science training is to help students and working professionals learn and master this latest technology which is of great demand in the market today.
Probability and Sampling distribution, Hypothesis testing focuses on helping students get on boarded to the idea of working on the data science applications and technologies. Extensive focus on understanding and processing Data is done to enable students work on data mining frameworks.

Programming language like Python, R-programming and Machine learning and focused to help young data scientist to design and develop applications and framework for working on data processing and analytical applications.

Multiple cases studies and use-case assignment are worked upon to get practical real-time understanding of the science of data processing.

Data Science training program can be picked up by anyone with statistical and engineering background with good hands on with programming. The course is applicable to:

Engineer Graduates
Working IT professional from programming, web development and DBA fields
Software programmers
JAVA developers
.NET developers

Data science open up opportunities in multiple discipline and ensure rewarding career opportunities to people with the right skill set. Data scientist can grab positions like:

  • Data Engineer
  • Machine learning engineer
  • Data science Generalist
  • Data analysts

With organization recognizing the importance of data mining and data processing to have predictive model for businesses and design forecasting models, the need of data scientist has been on a upward swing and data scientist is listed as one of the top skill set to possess over the coming decade.

Data science is for people with programming background. You need to have good programming skills and have sound understanding of Data management and processing and analysis. People from non-programming background with a flair for understanding programming and Data management and good understanding of statistical applications can also get into this program.

Data Science Course Curriculum

Course 1. Statistics (Mathematics for Data Science)

Duration: 45 Hours

  • Data, Data Types
  • Meaning of variables
  • Central Tendency
  • Measures of Dispersion
  • Measures of Variability
  • Measures of Shape
  • Data Distribution
  • Correlation, Covariance
  • Practical Examples

  • Mean, Expected value
  • Binomial Random Variable
  • Normal Distribution
  • Poisson Random Variable
  • Continuous Random Variable
  • Discrete Random Variable
  • Practical Examples

  • Central Limit Theorem
  • Sampling Distributions for Sample Proportion, p-hat
  • Sampling Distributions for Sample Mean, x-bar
  • Z- Scores
  • Practical Examples

  • Type I and Type II Errors
  • Decision Making
  • Power
  • Testing for mean, variance, proportion
  • Practical Examples

  • Contingency Tables
  • Independent and Dependent
  • Pearson’s Chi-Square Test
  • Misuses of Chi-Squared Test
  • Measures of Association
  • Practical Examples

  • Analysis of Variance & Co-Variance
  • ANOVA Assumptions & Comparisons
  • F-Tests
  • Practical Examples

Course 2. Python

  • nstallation of Python framework and packages: Anaconda & pip
  • Working with Jupyter notebooks
  • Creating Python variables
  • Numeric , strings
  • logical operations
  • Lists
  • Dictionaries
  • Tuples
  • sets
  • Practice assignment

  • Writing for loops in Python
  • While loops and conditional blocks
  • List/Dictionary comprehensions with loops
  • Writing your own functions in Python
  • Writing your own classes and functions
  • Practice assignment

  • Numpy
  • Pandas
  • Seaborn
  • Scikit
  • Matplotlib

  • Need for data summary & visualization
  • Summarising numeric data in pandas
  • Summarising categorical data
  • Group wise summary of mixed data
  • Basics of visualisation with Seaborn
  • Inferential visualisation with Seaborn
  • Visual summary of different data combinations
  • Practice assignment

  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  • Transforming variables
  • Data processing
  • Practice

Course 3. R Programming

  • Installation of R & R Studio
  • Getting started with R
  • Basic and Advanced Data types in R
  • Variable operators in R
  • Working with R data frames
  • Reading and writing data files to R
  • R functions and loops
  • Special utility functions
  • Merging and sorting data
  • Practice assignment

  • Introduction exploratory data analysis
  • Need for data summary & visualization
  • Summarising numeric data
  • Summarising categorical data
  • R Packages for Exploratory Data Analysis (dplyr, plyr, gmodes, car, vcd, Hmisc,psych, doby etc)
  • R Packages for Graphical Analysis (base, ggplot, lattice,etc)
  • Basics of visualisation with ggplot
  • Inferential visualisation with ggplot
  • Visual summary of different data combinations ( Bar/pie/line chart/ histogram/ boxplot/scatter/density etc) Practice assignment

  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)

  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment

  • Data processing with dplyr package
  • Practice

Course 4. Machine Learning

  • An Approach to Prediction
  • Least Squares and Nearest Neighbors
  • Statistical Decision
  • Regression Models

  • The Gauss–Markov Theorem
  • Multiple Regression
  • Forward- and Backward-Stepwise Selection
  • Ridge Regression
  • Lasso Regression
  • Example using R / Python

  • Linear Regression of an Indicator Matrix
  • Linear Discriminant Analysis
  • Logistic Regression
  • Rosenblatt’s Perceptron Learning Algorithm
  • Example using R / Python

  • One-Dimensional Kernel Smoothers
  • Local Linear Regression
  • Local Polynomial Regression
  • Mixture Models for Density Estimation and Classification
  • Example using R / Python

  • Bias, Variance and Model Complexity
  • Optimism of the Training Error Rate
  • Vapnik–Chervonenkis Dimension
  • Cross-Validation

  • Bootstrap and Maximum Likelihood Methods
  • Relationship Between the Bootstrap and Bayesian Inference
  • The EM Algorithm
  • Bagging
  • Example using R / Python

  • Regression Trees
  • Classification Trees
  • Bump Hunting
  • MARS: Multivariate Adaptive Regression Splines
  • Example using R / Python

  • Steepest Descent
  • Gradient Boosting
  • Regularization
  • Interpretation
  • Example using R / Python

  • Fitting Neural Networks
  • Over fitting
  • Hidden Units
  • Multiple Minima
  • Single, Multi-Layer Perceptron
  • Example using R / Python

  • Support Vector Classifier
  • Generalizing Linear Discriminant Analysis
  • Flexible Discriminant Analysis
  • Penalized Discriminant Analysis
  • Example using R / Python

  • Prototype Methods
  • K-means Clustering
  • Vector Quantization
  • Gaussian Mixtures
  • k-nearest Neighbors
  • Example using R / Python

  • The Apriori Algorithm
  • Unsupervised as Supervised Learning
  • Generalized Association Rules
  • K-means Cluster Analysis
  • Hierarchical Clustering
  • Principal Components, Curves and Surfaces
  • Non-Linear Dimension Reduction
  • The Google Page Rank Algorithm

  • The Apriori Algorithm
  • Unsupervised as Supervised Learning
  • Generalized Association Rules
  • K-means Cluster Analysis
  • Hierarchical Clustering
  • Principal Components, Curves and Surfaces
  • Non-Linear Dimension Reduction
  • The Google Page Rank Algorithm
  • Example using R / Python

  • Variable Importance
  • Random Forests and Over fitting
  • Bias
  • Adaptive Nearest Neighbors
  • Example using R / Python

Certification

Quality Thought’s Data Science Certification Process:

  • Quality Thought will provide a certificate to the students who successfully completed their Data Science training. The certification will be provided within one week of the training completion.
  • The certification will be given to the students who have successfully completed their projects and assignments on time.

Frequently asked questions

Students can benefit from our State of the art lab infrastructure facilities at all our training centers across the city. Our lab facilities are available through the day on all working days. For our online students, they can connect to our servers and other lab facilities over the internet and practice. These facilities are available 24X7.
If the student misses out of attending any session, he or she can re-attend the session by:
1. Attending the same session in another batch if student is attending classroom based session.
2. For online sessions, recording of the classes can be accessed by the student at all time to help revisit and listen the sessions missed out.
All our trainer are real-time industry experts with minimum of 10+ years of experience. Complete profiles of our trainers are available for review at our center and students are free to come interact with them and know more about them, before enrolling for programs.
All training programs conducted by Quality Thought are available in 3 modes, instructor based classroom programs, instructor based live online training and self-paced video based training. Students can choose
All discounts are subject to case-to-case basis. Please feel free to meet our administration staff to have a better discussion on the same. We do offer a variety of discounts and concession to our students coming in from different backgrounds.

For all corporate training requirements please feel free to get in touch with our administration staff managing corporate marketing and interaction. We have of the finest programs and offer to corporate with best-in-class programs.

Data Science Training Reviews

Data Science is a new and emerging technology and I’m very happy to have decided to do this program with Quality Thought. They really did justice to the training.
Sumanth

I took part in the Data Science training program recently and it was conducted very well. The faculty was very good and helped us understanding all the topics nicely.
Shivani

Attended Data Science training with Quality Thoughts recently. The program was really good and informative and the faculty was also good and did a great job.
Anup

The Data Science training program at Quality Thoughts is simply superb. I underwent the program recently and I’m very satisfied with the high quality delivery.
Sakshi

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