Course Description

Data Science Certification from SGIT, Steinbeis University, Germany:

Accelerate your career with Data Science certification from SGIT, Steinbeis University Germany , one of the leading universities in Germany. This course is a perfect blend of theory, case studies and capstone projects. The course curriculum has been designed by Steinbeis University and considered to be the best in the industry. Get noticed by recruiters across the globe with the international certification. Post certification one will gain the alumnus status in Steinbeis University.

What is the certification process?

Post completion of the training, one should take an online examination facilitated by the university and should attain 60% or more to complete the course and gain the certification. Subsequently participants can check their alumnus status on SGIT , Steinbeis Global Institute Tübingen.

Data Science Course

ExcelR offers Data Science course, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Data Science training. ExcelR is considered as the best Data Science training institute which offers services from training to placement as part of the Data Science training program with over 400+ participants placed in various multinational companies including E&Y, Panasonic, Accenture, VMWare, Infosys, IBM, etc. ExcelR imparts the best Data Science training and considered to be the best in the industry.

Why Should You Choose ExcelR For Data Science Training?

If you are serious about a career pertaining to Data science, then you are at the right place. ExcelR is considered to be one of the best Data Science training institutes. We have built careers of thousands of Data Science professionals with 97.3% placement record in various MNCs in India and abroad. “Training to Job Placement” – is our niche. We do the necessary hand-holding until you are placed. Our expert trainers will help you with upskilling the concepts, to complete the assignments and live projects.

ExcelR has a dedicated placement cell and has partnered with 150+ corporates which will facilitate the interviews and help the participants in getting placed. ExcelR is the training delivery partner in the space of Data Science for 5 universities and 40+ premier educational institutions like IIM, BITS Pilani, Woxen School of Business, University of Malaysia, etc. Faculty is our strength. All of our trainers are working as Data Scientists with over 15+ years of professional experience. Majority of our trainers are alumni of IIT, ISB and IIM and a few of them are PhD professionals. Owing to our faculty, ExcelR’s certification is considered to be the best Data Science certification offered in this space. ExcelR offers a blended learning model where participants can avail themselves classroom, instructor-led online sessions and e-learning (recorded sessions) with a single enrollment. A combination of these three modes of learning will produce a synergistic impact on learning. One can attend an unlimited number of instructor-led online sessions from different trainers for 1 year at no additional cost. No wonder ExcelR is regarded as the best Data Science training institute to master Data Science concepts and crack a job.

What Is Data Science? Who Is Data Scientist?

Data Science is all about mining hidden insights of data pertaining to trends, behaviour, interpretation and inferences to enable informed decisions to support the business. The professionals who perform these activities are said to be a Data Scientist / Science professional. Data Science is the most high-in-demand profession and as per Harvard and the most sort after profession in the world.

Why One Should Take The Data Science Course?

Is Data Science certification being worth pursuing as a career?

The answer is a big YES for myriad reasons. Digitalization across the domains is creating tons of data and the demand for the Data Science professionals who can evaluate and extract meaningful insights is increasing and creating millions of jobs in the space of Data Science. There is a huge void between the demand and supply and thereby creating ample job opportunities and salaries. Data Scientists are considered to be the highest in the job market. Data Scientist career path is long-lasting and rewarding as the data generation is increasing by leaps and bounds and the need for the Data Science professionals will increase perpetually.

1.4 Lakh jobs are vacant in Data Science, Artificial Intelligence and Big Data roles according to NASSCOM
The world will notice a deficit of 2.3 Lakh Data Science professionals by 2021
The Demand for Data Scientist professionals has increased by 417% in the year 2018, in India, as per the Talent Supply Index
Data Science is the best job to pursue according to Glassdoor 2018 rankings
Harvard Business Review stated that ‘Data Scientist is the sexiest job of the 21st century’

You May Question If Data Science Certification Is Worth It?

The answer is yes. Data Science / Analytics creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing the historical data in order to make informed decisions and improve their business. Digitalization in all the walks of the business is helping them to generate the data and enabling the analysis of the data. This is helping to create myriad data science/analytics job opportunities in this space. The void between the demand and supply for the Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. Data Scientist career path is long and lucrative as the generation of online data is perpetual and growing in the future.

Why ExcelR Is The Best Data Science Training Institute?

ExcelR offers the best Data Science certification online training along with classroom and self-paced e-learning certification courses. The complete Data Science course details can be found in our course agenda on this page.

Who Should Do The Data Science Course?

Professionals who can consider Data Science course as a next logical move to enhance in their careers include:

Professional from any domain who has logical, mathematical and analytical skills
Professionals working on Business intelligence, Data Warehousing and reporting tools
Statisticians, Economists, Mathematicians
Software programmers
Business analysts
Six Sigma consultants
Fresher from any stream with good Analytical and logical skills

Interview Preparation Sessions

Participants who have completed the Data Science course training and the projects will be put under our Placement Incubation Program. As part of this program, participants will undergo a thorough interview preparation process on Data Science. A huge repository of Data Science Interview questions with answers will be provided for the participants to prepare. A dedicated Data Science Subject Matter Expert (SME) will help in resume building, conduct mock interviews and evaluate each participant's knowledge, expertise and provide feedback. Our SMEs will do the necessary handholding on interview preparation process till the time the participant is placed. Guidance is also provided on Linkedin profile building and tricks of the trade to improve the marketability of the resume. - ExcelR Management

Things You Will Learn

Module 1 - Data Science Project Lifecycle

Recap of Demo
Introduction to Types of Analytics
Project life cycle

Module 2 - Introduction to R and Python Basic Statistics

Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.

Topics

High-Level overview of Data Science / Machine Learning project management methodology
Videos for Data Collection - Surveys and Design of Experiments will be provided
The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
Random Variable and its definition
Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts. Also, learn about some graphical techniques in Analytics.

Topics

Balanced vs Imbalanced datasets
Various sampling techniques for handling balanced vs imbalanced datasets
Videos for handling imbalanced data will be provided
What is Sampling Funnel, its application and its components
- Population
- Sampling frame
- Simple random sampling
- Sample
Measure of central tendency
- Mean / Average
- Median
- Mode
Measure of Dispersion
- Variance
- Standard Deviation
- Range
Expected value of probability distribution

Description: Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.

Topics

Measure of Skewness
Measure of Kurtosis
Various graphical techniques to understand data
- Bar plot
- Histogram
- Box plot
- Scatter plot
Introduction to R and RStudio
Installation of Python IDE
Anaconda and Spyder
Working with Python and R with some basic commands

Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.

Topics

Normal Distribution
Standard Normal Distribution / Z distribution
Z scores and Z table
QQ Plot / Quantile-Quantile plot

Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.

Topics

Sampling Variation
Central Limit Theorem
Sample size calculator
T-distribution / Student's-t distribution
Confidence interval
- Population parameter - Standard deviation known
- Population parameter - Standard deviation unknown

Introduction to R and Python basic stats

Module 3 - Hypothesis Testing

Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.

Topics

Parametric vs Non-parametric tests
Formulating a Hypothesis
Choosing Null and Alternative hypothesis
Type I and Type II errors
Comparative study of sample proportions using Hypothesis testing
2 sample t test

Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.

Topics

1 sample t test
1 sample z test
ANOVA
2 Proportion test
Chi-Square test
Non-Parametric test

Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform different tests and interpret the results.

Topics

Non-Parametric test continued
Hypothesis testing using Python and R

Module 4 - Linear Regression

Scatter Diagram
Correlation Analysis
Principles of Regression
Introduction to Simple Linear Regression
R shiny and Python Flask
- Introduction to R shiny and Python Flask (deployment)
Multiple Linear Regression

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

Topics

Scatter diagram
- Correlation Analysis
- Correlation coefficient
Ordinary least squares
Principles of regression
Splitting the data into training, validation and testing datasets
Understanding Overfitting (Variance) vs Underfitting (Bias)
Generalization error and Regularization techniques
Introduction to Simple Linear Regression
Heteroscedasticity / Equal Variance

Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.

Topics

LINE assumption
- Collinearity (Variance Inflation Factor)
- Linearity
- Normality
Multiple Linear Regression
Model Quality metrics
Deletion diagnostics

Module 5 - Logistic Regression

Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

Topics

Principles of Logistic Regression
Types of Logistic Regression
Assumption and Steps in Logistic Regression
Analysis of Simple Logistic Regression result

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.

Topics

Multiple Logistic Regression
Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1
Receiver operating characteristics curve (ROC curve)
Lift charts and Gain charts

Module 6 - Regularization Techniques

Lasso and Ridge Regressions

Module 7 - Multinomial Regression

Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.

Topics

Logit and Log Likelihood
Category Baselining
Modeling Nominal categorical data
Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables

Data Mining Unsupervised

Module 8 - Data Mining Unsupervised - Clustering

Description: As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchial clustering, K means clustering using clustering examples and know what clustering machine learning is all about.

Topics

Hierarchial

Supervised vs Unsupervised learning
Data Mining Process
Measure of distance
- Numeric - Euclidean, Manhattan, Mahalanobis
- Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
- Mixed - Gower’s General Dissimilarity Coefficient
Types of Linkages
- Single Linkage / Nearest Neighbour
- Complete Linkage / Farthest Neighbour
- Average Linkage
- Centroid Linkage
Hierarchical Clustering / Agglomerative Clustering

Description: In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.

Topics

Non-clustering
- K-Means Clustering
- Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
- Choosing the ideal K value using Scree plot / Elbow Curve
Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)

Module 9 - Dimension Reduction

Description: Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and SVD.

Topics

Why dimension reduction
Advantages of PCA
Calculation of PCA weights
2D Visualization using Principal components
Basics of Matrix algebra
SVD – Decomposition of matrix data

Module 10 - Data Mining Unsupervised - Network Analytics

Description: Under data mining unsupervised techniques, learn about Network Analytics and the measures used. Get introduced to Network Analysis tools like NodeXL.

Topics

Definition of a network (the LinkedIn analogy)
Measure of Node strength in a Network
- Degree centrality
- Closeness centrality
- Eigenvector centrality
- Adjacency matrix
- Betweenness centrality
- Cluster coefficient
Introduction to Google Page Ranking

Module 11 - Data Mining Unsupervised - Association Rules

Description: Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.

Topics

What is Market Basket / Affinity Analysis
Measure of association
- Support
- Confidence
- Lift Ratio
Apriori Algorithm
Sequential Pattern Mining

Module 12 - Data Mining Unsupervised - Recommender system

Description: Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.

Topics

User-based collaborative filtering
Measure of distance / similarity between users
Driver for recommendation
Computation reduction techniques
Search based methods / Item to item collaborative filtering
SVD in recommendation
Vulnerability of recommender systems

Module 13 - Text Mining

Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.

Topics

Sources of data
Bag of words
Pre-processing, corpus Document-Term Matrix (DTM) and TDM
Word Clouds
Corpus level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
Semantic network
Clustering

Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.

Topics

Extract Tweets from Twitter
Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor

Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and

Topics

Install Libraries from Shell
Extraction and text analytics in Python

Module 14 - Natural Language Processing

Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.

Topics

LDA
Topic Modeling
Sentiment Extraction
Lexicons and Emotion Mining

Classifiers

Module 15 - Machine Learning Classifiers - KNN

Description: Learn about Machine Learning modeling using KNN, the K nearest neighbour algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms.

Topics

Deciding the K value
Building a KNN model by splitting the data
Understanding the various generalization and regulation techniques to avoid overfitting and underfitting

Module 16 - Classifier - Naive Bayes

Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.

Topics

Probability – Recap
Bayes Rule
Naive Bayes Classifier
Text Classification using Naive Bayes

Module 17 - Decision Tree and Random Forest

Description: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples and Random Forest algorithms.

Topics

Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
Greedy algorithm
Measure of Entropy
Attribute selection using Information Gain
Ensemble techniques
Decision Tree C5.0 and understanding various arguments
Random Forest and understanding various arguments

Module 18 - Bagging and Boosting

Description: Bagging and Boosting is an ensemble technique which is a part of the random forest algorithm. Learn about Bagging and Boosting examples under this tutorial.

Topics

Boosting / Bootstrap Aggregating
AdaBoost / Adaptive Boosting
Stacking
Gradient Boosting
Extreme Gradient Boosting (XGB)

Module 19 - Black Box Methods

Description: Artificial Neural Network and Support Vector Machines are the two powerful Deep learning algorithms. Get introduced to Neural Net, Convolutional Neural Network, Recurrent Neural Network. Learn how to work with Support Vector Machine, SVM classifiers and SVM regression.

Topics

Artificial Neural Network
Biological Neuron vs Artificial Neuron
ANN structure
Activation function
Network Topology
Support Vector Machines
Classification Hyperplanes
Best fit “boundary”
Kernel Trick

Module 20 - Survival Analysis

Concept with a business case

Module 21 - Forecasting

Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.

Topics

Introduction to time series data
Steps of forecasting
Components of time series data
Scatter plot and Time Plot
Lag Plot
ACF - Auto-Correlation Function / Correlogram
Visualization principles
Naive forecast methods
Errors in forecast and its metrics
Model Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
Model-Based approaches
AR (Auto-Regressive) model for errors
Random walk
ARMA (Auto-Regressive Moving Average), Order p and q
ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
Data-driven approach to forecasting
Smoothing techniques
- Moving Average
- Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / HoltWinters
De-seasoning and de-trending
Econometric Models
Forecasting Best Practices
Forecasting using Python
Forecasting using R

Assignments/Projects/Placement Support

Module 22 - Assignments

Basic Statistics
- Data types Identification and probability
- Expected values, Measures of central tendencies
- Skewness and Kurtosis & Boxplot
- Practice Mean, Median, Varience, Standard Deviation and Graphical representations in R
- Creating Python Objects
- Practice Mean, Median, Varience, Standard Deviation and Graphical representations in Python
- Confidence intervals and distributions
Hypothesis Testing
- Buyer ratio
- Customer Order Form
- Cutlets
- Pantaloons
- Lab TAT
Linear regression
- Prediction of weight based on Calories consumed
- Delivery Time period Vs Sorting time
- Employee Churn rate Vs Salary
- Salary Prediction
R shiny and Flask
- Practice R shiny and Python Flask for Linear Regression assignments
Multiple Linear Regression
- 50 startups case study
- Computer data Case study
- Toyota Corolla
Logistic Regression
- Term deposit case study
- Elections results Case study
Multinomial Regression
- Student Program Case study
Hierarchical Clustering
- Crime data
- Eastwest Airlines
K means Clustering
- Insurance policy
- Crime data
PCA
- Dimension Reduction for Wine data
Network Analytics
- Node Properties practice in R
Association Rules
- Association Rules for Book store
- Association Rules for Mobile store
- Association Rules for Retail Transactions
Recommendation Engine
- Recommend Jokes for subscribers
Text mining, Web Extraction
- Extraction of tweets from twitter
- Reviews from ecommerce websites
Text mining
- Sentiment Analysis on extracted data
NLP
- Emotion mining by extracting a speech or novel from web
Naive Bayes
- Spam and Ham classifications
KNN Classifier
- Types of Glass
- Classification of Animals
Decision Tree and Random Forest
- Fraud Check
- Sales prediction of an Organization
XGB and GLM
- Social Networks Ads
Lasso and Ridge Regression
- Practice Lasso and Ridge with multiple Linear Assignments
ANN
- Forest Fires case study
SVM
- Classification of Alphabets
Survival analysis
- Prediction of Patient survival probability
Forecasting model based
- Airlines Forecasting
- Forecasting of sales for a soft drinks case study
Forecasting
- Forecasting of Bike shares
- Forecasting of Solar power consumption

Module 23 - Projects

Industry : Aviation
Predicting the flight delays
- How to determine which flights would be delayed and by how long?

Industry : Manufacturing
Predict impurity in ore
- The main goal is to use this data to predict how much impurity is in the ore concentrate As this impurity is measured every hour if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers giving them early information to take actions

Industry : Oil and Gas
Predicting the oil price
- Oil production and prices data are for 1932-2014(2014 data are incomplete );gas production and prices are for 1955-2014 export and net export data are for 1986-2013

Industry : Automotive
Electric Motor Temperature
- Predict the temperature of rotor and stator of E-Motor

Industry : Daily Analysis of a product
"Daily" Twitter Data Analysis for a Product
- Sentiment Emotion mining of twitter data of new product

Industry : E commerce
Natural Language Processing
- Top 5 relevant answers to be retrived based on input question

Module 24 - Resume Prep and Interview support

Resume Preparation
Interview Support

Value added courses

Module 25 - Basics of Hadoop and Spark

Introduction to Big Data
Challenges in Big Data and Workarounds
Introduction to Hadoop and its Components
Hadoop components and Hands-on
Understand the MapReduce (Distributed Computation Framework) and its Drawback
Introduction to Spark
Spark Components
Spark MLlib and Hands-on (one ML model in spark)

Module 26 - Basics of R

Introduction to R Programming

Introduction to R
Data Types in R

How To Install R & R Studio

Data Structures in R

Variable in R
R-Overview
- Vector
- Matrix
- Array
- List
- Data-Frame
Operators in R
- arithmetic
- Relational
- Logical
- Assignment
- Miscellaneous
Conditiional Statement
- Decision Making<
  - IF Statement
  - IF-Else Statement
  - Nested IF-Else Statement
  - Switch Statement
- Loops
  - While Loop
  - Repeat Loop
  - For Loop
- Strings
- Functions
  - User-defined Function
  - Calling a Function
  - Calling a Function without an Argument
  - Calling a Function with an Argument

Programming Statistical

Box Plots
Bar Charts
Histogram
Pareto Chart
Pie Chart
Line Chart
Scatterplot

How to Import Dataset in R

Read CSV Files
Read Excel Files
Read SAS Files
Read STATA Files
Read SPSS Files
Read JSON Files
Read Text Files

R-Packages

DpLyr
Hmisc or mise
Ggplot2
Caret
Data Table

How to Integrate R and SQL

How to Get Data From SQL to R

Module 27 - Basics of Python

Introduction to python

Description: You will get an introduction to the Python programming language and understand the importance of it. How to download and work with Python along with all the basics of Anaconda will be taught. You will also get a clear idea of downloading the various Python libraries and how to use them.

Topics

Introduction to Python
Installation of Anaconda Python
Difference between Python2 and Python3
Python Environment
Operators
Identifiers
Exception Handling (Error Handling)

Basic Python

Description: In this module, you will learn the basics such as assigning a variable, differences between dictionary, sets, tuple, and some decision making statements. Also, you will learn about working with different loops, data types and its usage.

Topics

Data Types
Conditional Statements
Functions
Loops

Working with libraries like NumPy, Pandas, Matplotlib, Seaborn, SciPy, Sklearn In Python

Description: This module helps you to learn and understand the different libraries used in Python. You will also get a clear idea about the NumPy library and how you can use it. NumPy is a Numeric Python library which helps in dealing with the numeric calculations with data frames.

Topics

NumPy Introduction
Arrays
Array Indexing
NumPy Data Types
Treating Missing and NA’s
Reshaping and combining Arrays

Working experience with Pandas In Python

Description: In this module, you will learn how to download the Pandas package and syntax for the same. Pandas is also a library similar to Numpy which predominantly helps in working with series data and data frames. You will learn how to impute the data in the place of missing values called the missing value treatment done in the Pandas package itself.

Topics

Pandas Introduction
Basic Operations on Series
Dataframe
Working with Text Data
Working with Missing Data
Indexing and Selecting Data
Merge, Join and Concatenate

Working experience with Matplotlib library In Python

Description: In this module, you will learn where, how and when to use the Matplotlib library. This library is used to visualize the data. You will get an in-depth understanding of the importance of this library.

Topics

Introduction to Matplotlib
Matplotlib design and different visualizations

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

Introduction to Seaborn Library
Visualizing the Distribution of the Datasets
Plotting the Categorical Data
Visualizing Linear Relationships
Visualizing Statistical Relationships

To work with Seaborn Library (High-level interface for drawing attractive and informative statistical graphics) In Python

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

Introduction to Seaborn Library
Visualizing the Distribution of the Datasets
Plotting the Categorical Data
Visualizing Linear Relationships
Visualizing Statistical Relationships

Introduction to SciPy and Sklearn Libraries In Python

Description: In this module, you will understand the importance of both Scipy and Sklearn libraries which are predominantly used in building Machine Learning Algorithms working with Linear Equations. Sklearn also known as Scikit-learn, is a machine learning library for the Python programming language. You will get a clear idea of where you can use these libraries along with some examples.

Topics

Installing both SciPy and Sklearn Libraries
Introduction to SciPy (Mathematical Algorithms)
Introduction to Sklearn (Machine Learning Algorithms)

Module 28 - Basics of MYSQL

Introduction to What is DataBase
Difference between SQL and NOSQL DB
How to Install MYSQL and Workbench
Connecting to DB
Creating to DB
What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
Select statement and using Queries for seeing your data
Joining 2 tables
Where clause usage
Indexes and views
Different operations in SQL
How to Connect to your applications from MYSQL includes R and Python