Data Science Courses in Ahmedabad, INDIA

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.

Yes. DataMites has 6-month no-cost EMI option. You can avail it directly while paying on the DataMites website at checkout.

No, most of software are free and open source. The guidelines to setup software is a part of course.

Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected location such as Singapore – Singapore, Bangalore-India, Hyderabad - India, Amsterdam – Netherlands, Houston – USA. Please check with the co-ordinators about training options in your location.

IABAC™ Exam fee is usually bundled as a part of total course fee. Please check with the co-ordinators for confirming the same.

All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.

We have a dedicated PAT (Placement Assistance team) to provide 100% support in your data science career pursuit. Check out PAT services