Data Science Courses in Vijayawada, INDIA

DataMites™ Certified Data scientist is designed to provide a right blend of all four facets of Data Science

• This four facets forms four pillars for data science field. They are 1. Programing 2. Statistics 3. Machine Learning 4. Business Knowledge.
• The course is mainly focussed on Python for core data science programing; it also includes R as necessary to enable professionals working in R.
• Statistics are covered as required for a Data Scientist, you may find detailed syllabus in syllabus tab.
• Machine Learning is the main tool kit for Data Science in predicting classification or regression.
• This course courses all popular ML algorithms as detailed in syllabus tab.
• This course allows candidates to obtain an in-depth knowledge by laying a strong foundation and covering all the latest data science topics.
• The increasing demand curve for data science professionals to manage the large set of data in various organizations providing millions of job opportunities in global markets.
• The knowledge gained through this course along with IABAC™ certificate surely help you to become data science professional.

This course comes as a perfect package of required Data Science skills including programing, statistics and Machine Learning. If you aspire to be Data Science professionals, this course can immensely help you to reach your goal.

After successful completion of this “Data Science Training in Vijayawada” course, you should have

• Gained a better knowledge on entire Data Science project work flow.
• Understand key concepts of statistics
• Gained hands on knowledge of popular Machine learning algorithms
• In depth knowledge on Data Mining, Data forecasting, and Data Visualization.
• Able to create business case for Data Science project
• Deliver end to end data science project to customer

Data science is the hottest field in the market as on today. Be it a small company or an MNC, they need a Data scientist to manage their large pool of data.

• High demand for data scientists with only a few qualified people to hire
• High salaries, nearly twice of an average software engineer as per Glassdoor report
• This course not only designed to enable you for new career opportunities but also allows you to apply the new age skills in your current work and become valuable to in your current role.
• Be assured that you are entering the future of data science much earlier to grab those wonderful opportunities arising from this biggest need of the business world.

This Data Science course is not restricted to any specific domain.

• Fresh Graduates or students from any discipline can choose this course to obtain better job opportunities in this most demanding data science field
• Working professionals looking to change their domain to data science field.
• Highly recommended for those who are aspiring jobs that mainly revolves around data analytics and machine learning

Project managers aspiring to switch to manage Data Science projects

DataMites™ is the global institute for Data Science accredited by International Association of Business Analytics Certifications (IABAC). DataMites provides flexible learning options from Classroom training, Live Online to high quality recorded sessions

The 6 Key reasons to choose Data Mites™

IABAC™ Accredited

• Globally reputed certification
• Syllabus Aligned with IABAC global market standards

Elite Faculty & Mentors

• Best in industry faculty from IIMs
• Course structured by Professors in Data Science from top universities
• Ensures high quality learning experience

Learning Approach

• Learning through case study approach
• Theory → Hands On → Case Study → Project → Model Deployment

10+ Industry Projects

• 10+ Industry related projects
• Enabling candidates to gain real time skills, also boosting confidence for real challenges

PAT (Placement Assistance Team)

• Dedicated PAT (Placement Assistance Team)
• Resume assist service
• Mapping candidates to verified jobs by PAT team
• Supporting in Interview preparation

24x7 Cloud Lab for ONE year

• High capacity data science cloud lab
• All Machine Learning python and R scripts on cloud lab for quick reference
• Enable participants to practice Data Science even with their mobile phones through cloud lab

The Data Science and Big Data analytics sector are poised for exponential growth in India, boosting immense job opportunities in the IT sector. Especially in Vijayawada, there will be a lot of opportunities to work in data science in the next 20 years and a significant chunk of the workforce in the IT would need to re-skill themselves in data science so that they can make the most of this employment boom.

Information Technology companies have started arriving at Vijayawada and are already training engineering students in Data Science technologies so that they could be absorbed by the IT companies. Whole IT ecosystem is boosting, and especially, Data Science professionals can get placements in some of the top IT companies here.

The following topics are covered here

Module 1 - Introduction to Data Science with Python

• Installing Python Anaconda distribution
• Python native Data Types
• Basic programing concepts
• Python data science packages overview

Module 2 - Python Basics: Basic Syntax, Data Structures

• Python Objects
• Math & Comparision Operators
• Conditional Statement
• Loops
• Lists, Tuples, Strings, Dictionaries, Sets
• Functions
• Exception Handling

Module 3 - Numppy Package

• Importing Numpy
• Numpy overview
• Numpy Array creation and basic operations
• Numpy Universal functions
• Selecting and retrieving Data
• Data Slicing
• Iterating Numpy Data
• Shape Manupilation
• Stacking and Splitting Arrays
• Copies and Views : no copy, shallow copy , deep copy
• Indexing : Arrays of Indices, Boolean Arrays

Module 4 - Pandas Package

• Importing Pandas
• Pandas overview
• Object Creation : Series Object , DataFrame Object
• View Data
• Selecting data by Label and Position
• Data Slicing
• Boolean Indexing
• Setting Data

Module 5 - Python Advanced: Data Mugging with Pandas

• Applying functions to data
• Histogramming
• String Methods
• Merge Data : Concat, Join and Append
• Grouping & Aggregation
• Reshaping
• Analysing Data for missing values
• Filling missing values: fill with constant, forward filling, mean
• Removing Duplicates
• Transforming Data

Module 6 - Python Advanced: Visualization with MatPlotLib

• Importing MatPlotLib & Seaborn Libraries
• Creating basic chart : Line Chart, Bar Charts and Pie Charts
• Ploting from Pandas object
• Saving a plot
• Object Oriented Plotting : Setting axes limits and ticks
• Multiple Plots
• Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors
• Satistical Plots with Seaborn

Module 7 - Exploratory Data Analysis: Case Study

The following topics are covered here

Module 1: Introduction to Statistics

• Two areas of Statistics in Data Science
• Applied statistics in business
• Descriptive Statistics
• Inferential Statistics
• Statistics Terms and definitions
• Type of Data
• Quantitative vs Qualitative Data
• Data Measurement Scales

Module 2: Harnessing Data

• Sampling Data, with and without replacement
• Sampling Methods, Random vs Non-Random
• Measurement on Samples
• Random Sampling methods
• Simple random, Stratified, Cluster, Systematic sampling.
• Biased vs unbiased sampling
• Sampling Error
• Data Collection methods

Module 3: Exploratory Analysis

• Measures of Central Tendencies
• Mean, Median and Mode
• Data Variability : Range, Quartiles, Standard Deviation
• Calculating Standard Deviation
• Z-Score/Standard Score
• Empirical Rule
• Calculating Percentiles
• Outliers

Module 4: Distributions

• Distribtuions Introduction
• Normal Distribution
• Central Limit Theorem
• Histogram - Normalization
• Other Distributions: Poisson, Binomial et.,
• Normality Testing
• Skewness
• Kurtosis
• Measure of Distance
• Euclidean , Manhattan and Minkowski Distance

Module 5: Hypothesis & computational Techniques

• Hypothesis Testing
• Null Hypothesis, P-Value
• Need for Hypothesis Testing in Business
• Two tailed, Left tailed & Right tailed test
• Hypothesis Testing Outcomes : Type I & II erros
• Parametric vs Non-Parametric Testing
• Parametric Tests , T - Tests : One sample, two sample, Paired
• One Way ANOVA
• Importance of Parametric Tests
• Non Parametric Tests : Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,
• Which Test to Choose?
• Ascerting accuracy of Data

Module 6: Correlation & Regression

• Introduction to Regression
• Type of Regression
• Hands on of Regression with R and Python.
• Correlation
• Weak and Strong Correlation
• Finding Correlation with R and Python

The following topics are covered here

Module 1: Machine Learning Introduction

• What is Machine Learning
• Applications of Machine Learning
• Machine Learning vs Artificial Intelligence
• Machine Learning Languages and platforms
• Machine Learning vs Statistical Modelling

Module 2: Machine Learning Algorithms

• Popular Machine Learning Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised Learning
• Application of Supervised Learning Algorithms
• Application of Unsupervised Learning Algorithms
• Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
• How to choose Machine Learning Algorithm?

Module 3: Supervised Learning

• Simple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• Multiple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• K-Nearest Neighbors : Theory, Implementing in Python (and R), KNN advantages, Working on use case.
• Decision Trees : Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case.

Module 4: Unsupervised Learning

• K-Means Clustering: Theory, Euclidean Distance method.
• K-Means hands on with Python (and R)
• K-Means Advantages & Disadvantages

The following topics are covered here

Module 1: Advanced Machine Learning Concepts

• Tuning with Hyper parameters.
• Popular ML algorithms,
• Clustering, classification and regression,
• Supervised vs unsupervised.
• Choice of ML algorithm
• Grid Search vs Random search cross validation

Module 2: Principle Component Analysis (PCA)

• Key concepts of dimensionality reduction
• PCA theory
• Hands on coding.
• case study on PCA

Module 3: Random Forest - Ensemble

• Key concepts of Randon Forest
• Hands on coding.
• Pros and cons.
• case study on Random Forest

Module 4: Support Vector Machine (SVM)

• Key concepts of Support Vector Machine.
• Hands on coding.
• Pros and Cons.
• case study on SVM

Module 5: Natural Language Processing (NLP)

• Key concepts of NLP.
• Hands on coding.
• Pros and Cons.
• Text Processing with Vectorization
• Sentiment analysis with TextBlob
• Twitter sentiment analysis

Module 6: Naïve Bayes Classifier

• Key concepts of Naive Bayes.
• Hands on coding.
• Pros and Cons
• Naïve Bayes for text classification
• New articles tagging

Module 7: Artificial Neural Network (ANN)

• Basic ANN network for Regression and Classification
• Hands on coding.
• Pros and Cons
• Case study on ANN, MLP

Module 8: Tensorflow overview and Deep Learning Intro

• Tensorflow work flow demo
• Introduction to deep learning.

Module 1: Tableau Introduction

• Tableau Interface
• Dimensions and measures
• Filter shelf
• Distributing and publishing

Module 2: Connecting to Data Source

• Connecting to sources, Excel, Data bases, Api , Pdf
• Extracting and interpreting data.

Module 3: Visual Analytics

• Charts and plots with Super Store data

Module 4: Forecasting

• Forecasting time series data

Module 1: Understanding Business Case

• Components of Business Case.
• ROI calculation techniques.
• Scoping

Module 2: Writing Data Science Business Case

• Defining Business opportunity.
• Translating to Data Science problem.
• Creating project plan

Module 3: Benefits Analysis

• Demonstrating break even and benefits analysis with Data Science Solutions.
• IRR benefits analyis
• Discounted Cash Flow

Module 4: Starting project, Setting up Team and closing

• Initiating Project
• Setting up the Team
• Controling project delivery
• Closing project.