DATA SCIENCE COURSE IN HYDERABAD
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract insights from data. Companies are investing lot of money on managing and analysing the data. So, Avail the best data science course in Hyderabad at our training institute. Also, become a certified data scientist under the guidance of esteemed trainer with real-time IT experience.
PROUD TO BE ASSOCIATED WITH...
Yes! We do provide recordings of the lectures to both Online and Classroom trainees for free of cost.
Candidates can avail educational loan from Gyandhan, if he/she is eligible. Students have the flexibility to pay the course fee in EMI’s. To check your eligibility feel free to call or whatsapp + 91 836-748-7105.
Basic graduation is fine to learn Data Science. Since most of our trainees are from Non-IT backgrounds like BTech Mechanical, etc., we will teach data science from basics. We assure one-on-one mentorship with the trainer as our intake is only 10 students per batch.
Yes. We do provide LOR for all countries, with which candidates can apply at Indian Embassy in their respective countries to avail student visa. Call or Whatsapp +91 836 748 7105 for more details.
We will make you attend for interviews post completion of 3.5 months training. More than that no one can do anything. Don’t believe in fake promises. It completely depends on candidates performance in interview. Don’t fall in trap like job guaranteed programmes by paying huge money.
Yes! We do conduct weekend batches especially for working professionals. Call +91 836 748 7105 for more details.
We don’t offer python programming language alone. We offer complete Data Science Package with Python skills ( Beginner to Advanced Level with DJANGO Framework) and Machine Learning. We suggest, don’t learn programming languages alone. Learn languages with applications. Data Science is one of the applications using Python Programming. No company will hire just with the knowledge of programming languages.
OUR ADVANCED COURSE CURRICULUM
The 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.
Mean, Median, Mode, Variance, Standard deviation, Probability, Permutations, Combinations, Bayes theorem, Null Hypothesis, Quartile, Interquartile, Measure of central tendency, correlation, causality, Sample, Population, Covariance, Pearson correlation, Random variables, Hypothesis, Types of Hypothesis, Significance value, Types of tests based on features of random variables, Chi square tests and ANOVA
PYTHON PROGRAMMING BASICS – Installing Jupyter Notebooks, Python Overview, Python 3 Overview, 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.
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 – Functions, Why Defining Functions? Calling Functions with Multiple Arguments, Anonymous Functions – Lambda Using Built-In Modules, User-Defined Modules, Decorators Iterators and Generators.
FILE I/O AND EXCEPTIONAL HANDLING – Opening and Closing Files, Open Function, File Object Attributes, Close Method, Read, Write. 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 – Array Creation, Printing Arrays, Basic Operations- Indexing, Slicing and Iterating Shape Manipulation – Changing shape, stacking and splitting of array Vector stacking.
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 –Plot, Controlling Line Properties, Working with Multiple Figures and Histograms.
Advanced Formulae (Eg: INDEX-MATCH, SUMPRODUCT), Pivot Tables and Pivot Charts, Power Query for Data Transformation, Power Pivot and Data Models, Advanced Data Visualization (Eg: Sparklines, Conditional Formatting), Dynamic Named Ranges, Advanced Data Validation, VBA and Macros for Automation, Solver and What-if Analysis, Statistical Analysis Tools (Eg: Regression Analysis, ANOVA).
Data Modeling and Relationships, DAX (Data Analysis Expressions) for Advanced Calculations, Custom Visuals and Visualizations, Power Query for Data Transformation and Shaping, Data Aggregation and Summarization, Time Intelligence Functions, Advanced Filters and Slicers, Row-Level Security, Performance Optimization Techniques, Integration with other Data Sources (Eg: SQL and Azure).
Introduction to SQL, Retrieving Data, Updating Data, Inserting Data, Deleting Data, Sorting and Filtering Data, Create connection to the data base using python, Creating a data base, Check if data base exists, Creating a table, Check if table exists and Select records from the table with python
Install Tableau, Tableau to Analyze Data, Connect Tableau to variety of datasets, Analyze, Blend, Join and Calculate Data, Tableau to Visualize Data, Visualize Data In the form of Various Charts, Plots, and Maps, Data Hierarchies, Work with Data Blending in Tableau, Work with Parameters, Create Calculated Fields, Adding Filters and Quick Filters, Create Interactive Dashboards, and Adding Actions to Dashboards
Collecting data from different sources, Analyzing data, Data preprocessing, Data munging, Data mining, Data manipulation, Data visualization, Feature Selection, Feature Scaling and Dimensionality reduction
Forecasting – Predicting the future, Classification – Categorize a series, Segmentation – Breaking a series into periods of distinct characteristics, Anomaly Detection – Identifying unexpected observations, Signal Processing – Extracting signal from noise, Geospatial-Temporal Analysis – Analyzing time series with a location component
INTRODUCTION TO MACHINE LEARNING – Machine Learning? What is the Challenge? Supervised Learning and Unsupervised Learning
LINEAR REGRESSION – Linear Regression with Multiple Variables, Disadvantage of Linear Models, Interpretation of Model Outputs, Understanding Covariance and Co linearity, Case study on Application of Linear Regression for housing price prediction.
LOGISTIC REGRESSION – Why Logistic Regression, 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 – 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.
RANDOM FOREST– Random Forest, data set, how to build Random Forest? Ensemble Techniques – Boosting, Bagging, Gradient Boost, XG Boost, Classification Rules, Regression Rules.
SUPPORT VECTOR MACHINE – Support Vector Machine, data set, how to build Support Vector Machine? Support Vectors, Marginal Planes and Distance, Parameter Tuning, Classification Rules, Regression Rules.
K NEAREST NEIGHBOURS – K Nearest Neighbors, data set, how to build K Nearest Neighbors? Data Set, Nearest Neighbors, Distance Between Two Points, Euclidian Distance and Manhattan Distance Methods, Choosing the Best K Value Classification Rules, Regression Rules.
NAÏVE BAYES– Naïve Bayes, data set, how to build Naïve Bayes? Data Set, Types of Events, Conditional Probability, Bayes Theorem Classification Rules. Practical Example of Bayes Theorem.
Hierarchical Clustering, k-Means algorithm, Principal Component Analysis (PCA), Apriori Algorith and DBSCAN Clustering.
Neural Network, Understanding Neural Network Model, ANN, CNN, RNN, Understanding Tuning of Neural Network, Case study using Neural Network.
Intro to Natural Language Processing (NLP), Speech to Text and Text to Speech Conversion using NLP
Define Problem Statement, Gather requirements from various sources, Data Pre-Processing, Choosing the right ML algorithm by considering it’s accuracy and Note the best performed algorithms.
LIBRARIES
WHY JOIN ADITI ???
- Only 10 students per batch are allowed.
- Agency 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.
For PYTHON / DATA SCIENCE / ARTIFICIAL INTELLIGENCE / MACHINE LEARNING / BLOCKCHAIN / DIGITAL MARKETING CORPORATE TRAINING IN YOUR ORGANISATION, send requirements to admin@aditidigitalsolutions.com
Aditi Digital Solutions, training institute cum organisation 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.
