DIPLOMA IN DATA SCIENCE COURSE

The following topics are covered here

Module 1 - Introduction to Data Science with Python

• Installing Python
• Programming basics
• Native Data types

Module 2 - Python Basics: Basic Syntax, Data Structures

• Data objects
• Math
• Comparison Operators
• Condition Statements
• Loops
• Lists
• Tuples
• Sets
• Dicts
• Functions

Module 3 - Numpy Package

• Numpy overview
• Array
• Selecting Data
• Slicing
• Iterating
• Manuplications
• Stacking
• Splitting Arrays
• Functions

Module 4 - Pandas Package

• Pandas overview
• Series and DataFrame
• Manuplication

Module 5 - Python Advanced: Data Mugging with Pandas

• Histogramming
• Grouping
• Aggregation
• Treating Missing Values
• 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:

• Data Cleaning
• Data Wrangling

Module 8 - Exploratory Data Analysis: Case Study

The following topics are covered here

Module 1: SQL and RDBMS introduction

• Basics of SQL
• Essential commands to create and manage DB

Module 2: SELECT Query in SQL

• Retrieve data from SQL data base through complex select queries

Module 3: Connecting Tables in Data Base Query

• Left Join
• Right Join and Inner Join

Module 4: Python SQL query to retrieve from any SQL database

Module 5: Hands-On Project

• Project to retrieve data from live SQL server with queries as per the data requirement, in line with Data Science projects.

The following topics are covered here

Module 1: Introduction to Big Data

• What is Big Data?
• Why we need it?

Module 2: Big Data Concepts

• Core concepts of Big Data

Module 3: Hadoop Installation and configuration

• Hadoop Installation on various platforms.

Module 4: Hadoop – Simple use case deployment

• Simple use-case with Hadoop

The following topics are covered here

Module 1: Data Science Introduction

• What is Data Science?
• Evolution of Data Science

Module 2: Data Mining vs Business Analytics vs Data Science

• Difference between popular terminologies

Module 3: Classification of Business Analytics

• Descriptive
• Predictive
• Discovery and Prescriptive Analytics

Module 4: Artificial Intelligence vs Machine Learning

• Basic differences in AI and ML usage

Module 5: Types of Machine Learning

• Various Machine Learning methods

Module 6: Data Science Project Work Flow

• 6-step Process of Data Science projects

Module 7: Industry application of Data Science solutions

• Popular Industry applications of Data Science

Module 1: Introduction to Statistics

• Descriptive and Inferential Statistics.
• Definitions , terms, types of data

Module 2: Harnessing Data

• Types of Sampling Data.
• Simple random sampling, Stratified, Cluster sampling. Sampling error.

Module 3: Exploratory Analysis

• Mean, Median and Mode, Data variability, Standard deviation, Z-score, Outliers

Module 4: Distributions

• Normal Distribution, Central Limit Theorem, Histogram, Normalization, Normality tests, skewness, Kurtosis.

Module 5: Hypothesis & computational Techniques

• Hypothesis Testing, Null Hypothesis, P-value, Type I & II errors, parametric testing: t- tests, anova test, non-parametric testing

Module 6: Correlation & Regression

Module 1: Introduction to Pandas

• Pandas import
• Basic structure

Module 2: Series and DataFrame data structures

• Core data structure in Pandas Series and DataFrame

Module 3: Essential functions in Pandas for data mugging

• Basic Pandas functions

Module 4: Various Data Treatment Techniques

• Missing values
• Duplicates
• outliers etc.,

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Module 5: Exploratory Data Analysis with Pandas

• EDA for open dataset with Pandas

Module 6: Plotting with Pandas

• Pandas plot function in detail

Module 7: Transformation data to get it ready for Machine Learning

• Data treatment with Pandas introduction

Module 1: Machine Learning Introduction

• What is ML?
• ML vs AI
• ML workflow
• Statistical modeling of ML
• Application of ML

Module 2: Machine Learning Algorithms

• Popular ML algorithms
• Clustering
• Classification and Regression
• Supervised vs Unsupervised
• Choice of ML

Module 3: Supervised Learning

• Simple and Multiple Linear regression
• KNN, and more

Module 4: Linear Regression and Logistic Regression

• Theory of Linear regression
• Hands on with use cases

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Module 5: K-Nearest Neighbour (KNN)

• Theory of KNN
• Hands on with use cases

Module 6: Decision Tree

• Theory of Decision Tree
• Hands on with use cases

Module 7: Naïve Bayes Classifier

• Bayes Theorem
• Hands on Naïve Bayes implementation

Module 8: Unsupervised Learning

• K-means Clustering

Module 1: Advanced Machine Learning Concepts

• uning with Hyper parameters.
• Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised.
• Choice of ML

Module 2: Random Forest – Ensemble

• Ensemble theory, random forest tuning

Module 3: Support Vector Machine (SVM)

• Simple and Multiple Linear regression
• KNN

Module 4: Natural Language Processing (NLP)

• Text Processing with Vectorization
• Sentiment analysis with TextBlob
• Twitter sentiment analysis.

Module 5: Naïve Bayes Classifier

• Naïve Bayes for text classification
• New articles tagging

Module 6: Artificial Neural Network (ANN)

• Basic ANN network for regression and classification

Module 7: Tensorflow overview and Deep Learning Intro

• Tensorflow work flow demo and intro to deep learning.

Module 1: Introduction to Sentiment Analysis

• Sentiment Polarity

Module 2: Introduction to NLTK and TextBlob packages

• Hands on Sentiment Analysis with NLTK and TextBlob

Module 3: Application of Sentiment Analysis on Twitter live

• Connecting to Twitter API and Live hands on sentiment analysis use case

Module 1: Introduction to Deep Learning

• What is deep Learning. Deep Learning models

Module 2: Deep Learning with Python frameworks

• Keras
• TensorFlow

Module 3: Applications of Deep Learning

• Various applications of Deep Learning.

Module 1: Artificial Intelligence Introduction

• Core concepts of Artificial Intelligence

Module 2: Domains of Artificial Intelligence

• Computer Vision, NLP, ML & DL, Robotics

Module 3: Applications of Artificial Intelligence

• Various industry applications of AI

Module 4: Limitations of Artificial Intelligence

• Major limitations of AI Adoptions

Module 1: AI model deployment strategies

• Various model deployment strategies

Module 2: Simple API deployment

• API deployment with FLASK framework

Module 3: Creating website based on API deployed

• Creating HTML front-end for API

Module 1: Introduction to CNN

• Convolution – feature maps, max pooling, ANN

Module 2: Image Processing fundamentals

• Image Basics, Converting image to Numpy Array

Module 3: Convolution Filter Explanation

• Various kinds of filters – edge filter

Module 1: Introduction to Image classification coding

• Keras with TensorFlow, hands on image classification CNN

Module 2: Keras code for classifying Cats and Dogs

• Python Keras coding for image classification

Module 3: Creating predicting model with TensorFlow as backend.

• Complete CNN Code

Module 1: REST API

• API concepts
• Web servers
• URL parameters

Module 2: FLASK Web framework

• FLASK Web framework Installing flask
• configuration

Module 3: API in Flask

• API coding in Flask

Module 3: End to End Deployment

• Exporting trained model, creating end to end API.

DataMites™ has been focusing on building full Stack Data Science professionals with intense training that gives them all the tools, techniques, and core concepts needed to make a better impact in the Data World. DataMites™ Diploma in Data Science training is one such initiative focusing on young grads and fresher who are passionate about Data field and want to define their career in this fastest growing technology. This learning consists of all the essential areas such as Python, Statistics, Machine Learning, Time series foundation, Model deployment, Deep Learning, Tableau and Data Science business concepts that a Data scientists need to be specialized. It allows you to go through structured classroom training sessions regularly happening in Bangalore and lets you master the concepts under the guidance of industry experts.

DataMites™ Diploma in Data Science training is specially curated by industry experts to impart the best knowledge needed to kick start a most challenging Data Scientist role. Our course helps the fresher’s to

• Gain a thorough understanding of Data Science concepts that include Statistics, Machine Learning, Tableau, Deep Learning, Time series foundation and Data Science business concepts.
• Comprehensive knowledge of Machine Learning as it covers both Associate and Expert courses.
• Ability to perform Model Deployment independently.
• Exposure to real-life case scenarios with hands-on 3+ detailed Industry-related projects.
• 3-month Project Mentoring with unlimited Projects from diverse industries.
• Exposure to Large collection of interview questions.

DataMites™ Diploma in Data Science training will transform the freshers to a Data Science expert and successfully add value to their career. With exposure to detailed industry-related projects and project mentoring, it turns them into an industry-ready Data Scientist. During this DataMites™ Diploma in Data Science course, you will be trained in the classroom by our experts to

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

DataMites™ is the top training provider accredited by the International Association of Business Analytics Certifications (IABAC) who is offering Data Science courses. DataMites™ Data Science classroom course comes with the following benefits

• Global reputation since the syllabus is aligned with IABAC global market standards
• Elite instructors who are backed with years of industry experience in Data Science
• Structured learning approach with real-time exposure
• Exposure to different industry-related projects.
• 24/7 high capacity cloud lab to practice what that has been taught

DataMites™ provide flexible learning options from traditional classroom training, lastest virtual live classroom to distance course. Based on your location preference, you may have one or more learning options

This course is perfectly aligned to the current industry requirements and gives exposure to all latest techniques and tools. The course curriculum is designed by specialists in this field and monitored improved by industry practitioners on continual basis.

All certificates can be validated with your unique certification number at IABAC.org portal. You also get candidate login at exam.iabac.org , where can find your test results and other relevant validation details.

The results of the Exam are immediate, if you take online test at exam.iabac.org portal. The certificate issuance, as per IABAC™ terms, takes about 7-10 bussiness days for e-certificate.

No, the exam fees are already included in the course fee and you will not be charged extra.

Course fee needs to be paid in one payment as it is required to block your seat for the entire course as well as book the certification exams with IABAC™. In case, if you have any specific constrains, your relation manager at DataMites™ shall assist you with part payment agreements

DataMites™ has a dedicated Placement Assistance Team(PAT), who work with candidates on individual basis in assisting for right Data Science job.

You get 100% refund training fee if you the training is not to your satisfaction but the exam fee will not be refunded as we pay to accreditation bodies. If the refund is due to your availability concerns, you may need to talk to the relationship manager and will be sorted out on case to case basis

DataMites™ provides loads of study materials, cheat sheets, data sets, videos so that you can learn and practice extensively. Along with study materials, you will get materials on job interviews, new letters with latest information on Data Science as well as job updates.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

• 1. Job connect
• 2. Resume Building
• 3. Mock interview with industry experts
• 4. Interview questions