DATA SCIENCE COURSE IN HYDERABAD
Data science is the field of study that combines domain expertise, programming skills, and knowledge of math and statistics to extract insights from data. Companies are investing invest a lot of money on data science and it’s the most rewarding job in the 21st century. Avail best data science course in Hyderabad at our training institute and become a certified data scientist under the guidance of esteemed trainer with 18+ years real-time experience.
PROUD TO BE ASSOCIATED WITH...
NO SEATS AVAILABLE FOR 11TH SEP 2019 BATCH
No further requests entertained. FIRST COME FIRST SERVE is our motto. Strictly CLASSROOM TRAINING. NO ONLINE SESSIONS.
Dr Mohammed Habeebvulla
APPLICATION DEVELOPER | CORPORATE TRAINER | GOOGLE SCHOLAR
SPECIALTIES : Data Science Trainer, Python, Machine Learning and Sun Certified Java Professional
EXPERIENCE : Application Developer and Lecturer in Department of Computer Science
Al-Imam Muhammad Ibn Saud Islamic University
COUNTRIES : USA, UK, OMAN, DUBAI, SAUDI ARABIA, QATAR AND KUWAIT
EDUCATION : Calorx Teachers University
Doctor of Philosophy – PhD, Computer Science · (2015 – 2018)
M.C.A, Master of Computer Applications · (1996 – 1999)
OUR ADVANCED COURSE CURRICULUM
Data Science Overview, Data Science – Why all the excitement? Demand for
Data Science Professionals, Brief Introduction to Big data and Data Analytics, Life
cycle of data science, what does Data scientist Do. Tools and Technologies used in data Science.
FUNDAMENTALS OF MATHEMATICS AND PROBABILITY – Basic
understanding of linear algebra, linear regression, Matrices and Vectors, Addition
and Multiplication of matrices, Fundamentals of Probability, Probability distributed
function and cumulative distributed function, Problem solving using R for vector manipulation, Problem solving for probability assignments.
DESCRIPTIVE STATISTICS – Describe or summarize a set of data Measure of
central tendency and measure of dispersion, The mean, median, mode and
skewness, Computing Standard deviation and Variance, Types of distribution,
Sample covariance, Sample Covariance Matrix, Order statistics ,Exploratory analytics R Methods.
INFERENTIAL STATISTICS – What is inferential statistics? Different types of
Sampling techniques, Central Limit Theorem, Point estimate and Interval estimate, Creating confidence interval for population parameter, Characteristics of Z-distribution and T-Distribution, Basics of Hypothesis Testing, Type of test and rejection region, Type of errors in Hypothesis testing, Type-l error and Type-II errors, P-Value and Z-Score Method, T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA), Problem solving for C.L.T, Problem solving
Hypothesis Testing, Problem solving for T-test, Z-score test, Case study and model run for ANOVA, ANCOVA, Type of test and Rejection Region, Type o errors-Type 1 Errors, Type 2 Errors, P value method, Z score Method, Types of distribution, Exploratory analytics R Methods.
INTRODUCTION TO MACHINE LEARNING – What is Machine Learning? What is the Challenge? Introduction to Supervised Learning, Unsupervised Learning, what is Reinforcement Learning?
LINEAR REGRESSION -Introduction to Linear Regression, Linear Regression with Multiple Variables, Disadvantage of Linear Models, Interpretation of Model Outputs, Understanding Covariance and Co linearity, Understanding Heteroscedasticity, Case study on Application of Linear Regression for housing price prediction
LOGISTIC REGRESSION – Why Logistic Regression, Introduction to classification Cost function for logistic regression, Application of logistic regression to multi-class classification, Confusion Matrix, Odd’s Ratio and ROC Curve, Advantages and Disadvantages of Logistic Regression, Case study on To classify an email as spam or not spam using logistic Regression.
DECISION TREES AND SUPERVISED LEARNING – Decision Tree, data set, How to build decision tree? Understanding Kart Model, Classification Rules- Over fitting Problem, Stopping Criteria And Pruning, How to find final size of Trees? Model a decision Tree, Naive Bayes, Random Forests and Support Vector, Machines, Interpretation of Model Outputs, Business Case Study for Kart Model, Business Case Study for Random Forest, and Business Case Study for SVM
UNSUPERVISED LEARNING – Hierarchical Clustering, k-Means algorithm for
clustering, groupings of unlabeled data points, Principal Component Analysis (PCA), Independent components analysis(ICA), Anomaly Detection, Recommender System-collaborative filtering algorithm, Case study on Recommendation Engine for E-commerce/retail chain.
DEEP LEARNING – Neural Network, Understanding Neural Network Model, Understanding Tuning of Neural Network, Case study using Neural Network
NATURAL LANGUAGE PROCESSING – Introduction to Natural Language Processing (NLP), Word Frequency Algorithms for NLP Sentiment Analysis, Case Study on Twitter data analysis using NLP.
PYTHON PROGRAMMING BASICS – Installing Jupyter Notebooks, Python Overview, Python 2.7 vs Python 3, Python Identifiers, Various Operators and Operators Precedence, Getting input from User, Comments and Multi line Comments.
MAKING DECISIONS AND LOOP CONTROL – Simple if Statement, if-else
Statement, if-else-if Statement, Introduction to while Loops, Introduction to For Loops, Using continue and break.
PYTHON DATA TYPES : LIST, TUPLES AND DICTIONARIES – Python Lists, Tuples, Dictionaries, Accessing Values, Basic Operations, Indexing, Slicing, and Matrices, Built-in Functions & Methods, Exercises on List, Tuples and Dictionary.
FUNCTIONS AND MODULES -Introduction to Functions, Why Defining Functions? Calling Functions Functions with Multiple Arguments, Anonymous Functions – Lambda Using Built-In Modules, User-Defined Modules, Module Namespaces, Iterators and Generators
FILE I/O AND EXCEPTIONAL HANDLING – Opening and Closing Files, Open Function, File Object Attributes, Close Method , Read, Write, Seek. Exception Handling, the try-finally Clause, Raising an Exceptions, User-Defined Exceptions Regular Expression- Search and Replace, Regular Expression Modifiers, Regular Expression Patterns and Re module
NUMPY – Introduction to Numpy, Array Creation, Printing Arrays, Basic Operations- Indexing, Slicing and Iterating Shape Manipulation – Changing shape, stacking and splitting of array Vector stacking
PANDAS – Introduction to Pandas, Importing data into Python, Pandas Data Frames, Indexing Data Frames, Basic Operations With Data frame, Renaming Columns, Subletting and Filtering a data frame.
MATPLOTLIB –Introduction, Plot, Controlling Line Properties, Working with Multiple Figures and Histograms.
WHY JOIN ADITI ???
- Certified Trainers with 18+ Years Real Industry Experience.
- Only 10 students per batch are allowed.
- Company Driven Training with 100% placement assistance.
- Advanced Course Curriculum as per MNC requirements.
- Best faculty with Excellent Lab Infrastructure.
- Prepare your CV/Resume to attend Interviews and securing a Job.
- One-to-one Attention by Instructors.
- Classes with 30% theory and 70% hands-on.
- Successfully executed 30+ projects in just 3 months.
- Internship available.
WHO ARE ELIGIBLE ???
All Graduates, Post Graduates, IT Professionals, Business owners, Engineers, Technologists
and anyone who are really serious about their career
Aditi Digital Solutions, training institute cum agency is located only at KPHB Colony, Hyderabad.
Batch formation completely depends on first come first serve basis.
Only 10 students per batch are allowed. No further requests are entertained.
Beware of fraudsters with the name of ADITI offering discounts and advertisements.