Advanced Data Science Course with Artifical Intelligence in Marthahalli, Bengaluru.
Data Science Course is the latest trending technology in present technical world. Social Prachar is one of the Top Data Science Training Institute in Bangalore with Placement assistance. We will provide the Data Science training with Real time trainers, client case studies and live projects. Data Science course with IBM certification from Social Prachar will help to the Candidates to get in-depth knowledge of Data Science by laying the strong foundation for the career.
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- Data Science is the most awaited and promising career in 21st century. Data Science course is most useful for both beginner’s and Professional who has the aspiring to Data Science.
- Social Prachar provides the Data Science course training with R–Language , Tableau, Python , Machine Learning, Deep learning , Natural Language Process, Artificial Intelligence and Big Data with Real time Projects.
The course covers technical as well as business aspects of Data Science. This will helps to gain more industrial oriented knowledge regarding Data Science and other business aspects.
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- There is a wide variety of career opportunities available for Data Science Professionals because Data Scientists are required in different areas like Data Analytics , Data Research, Big Data and Data Management and more.
- Data Science Jobs has high range of Salaries. Minimum average salary ranges are from 5 Lakhs to 30 lakhs per annum.
Master Data Science with IBM certification program
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Data Science Course Highlights
Realtime Expert Trainers
Dedicated portal for Practice
Live Projects to work
Who to Join Data Science course
Data Analysts, Business Analysts,
Graduates, Post Graduates
Also, anyone having interest in Data Science is free to attend our workshop.
Data Science Job Roles Available
Business Intelligence Analyst
Data Mining Engineer
Senior Data Scientist
Data Science Job Market in 2019
Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
5,000+ unique job postings for data scientists each month.
3,00,000+ Skilled Data Scientists required by the End of 2019.
Salaries range from $60,000 and up.
GlassDoor ranked it top place for popular jobs
- 1 Free Demo Workshop on Data Science Tomorrow at 8:30 AM and 4 PM →
- 2 Key Highlights
- 3 Dedicated Portal
- 4 Internship Offers for Freshers
- 5 Weekly Assignments
- 6 Timely Doubt Sessions
- 7 Dedicated HR Team
- 8 Advanced Curriculums
- 9 1-1 Mentorship
- 10 Certificates
- 11 Resume Preparation Tips
- 12 Interview Guidance and Support
- 13 Get Trained by Experts from IIIT and AMITY university
- 14 Upcoming Batches
- 15 Start with a FREE Session
- 16 Master Data Science with IBM certification program
- 17 Call our Quick Helpline 8019 479 419 for Instant Help.
- 18 Start with a FREE Orientation Session
- 19 Call Our Career Advisor Now for Requisite Details.
- 20 Other Popular Courses
- 21 Frequently Asked Questions
- 21.1 What about Your Data Science Course Details ?
- 21.2 What type of Training Modes You Provide?
- 21.3 Who are Eligible to Learn Data Science?
- 21.4 What skills do I need to Master Data Science?
- 21.5 What is the Data Science Job Market Now ?
- 21.6 Do You Provide Job Placements ?
- 21.7 What Type of Certifications You Provide After the course ?
- 21.8 Do You Provide Hands-on Experience ?
- 21.9 What about Trainer Faculty experience ?
- 21.10 What is data Science ?
- 21.11 I am 2018 Fresher Am i Eligible to Learn Data Science course?
- 21.12 What are the Modules you cover in Data Science Course ?
- 21.13 I have 3+ Years IT experience, am i Eligible to Learn Data Science Course?
- 21.14 I'm from EEE background , can I go for data science , I don't have basics of any programming language
- 21.15 I don't have any engineering background can I still learn data Science.
- 21.16 Will u explain about the whole data science course including Python, R lang etc....?
- 21.17 Will this be best option for MBA finance students? How will it be helpful for finance students??
- 21.18 I'm weak at Mathematics, Can I opt Data Scientist as my career ?
- 21.19 I'm from Pharmacy background. Please suggest me whether am I applicable to do this course?
- 21.20 I'm from mechanical background, but i want to learn data science , is it possible to understand without knowledge in languages like c , java knowledge
- 21.21 Hello am from medical science field am I eligible for this course
- 21.22 Is there any minimum percentage criteria of graduates for jobs in data science
Data Science course Curriculum
|INTRODUCTION TO STATISTICS|
|Basic business statistics||How businesses use statistics |The basic vocabulary of statistics |The types of data used in business|
|Organizing and Visualizing Data||The sources of data used in business | To construct tables and charts for numerical data | To construct tables and charts for categorical data | The principles of properly presenting graphs|
|Numerical Descriptive Measures||To describe the properties of central tendency, variation, and shape in numerical data | To construct and interpret a boxplot | To compute descriptive summary measures for a population | To compute the covariance and the coefficient of correlation|
|Basic Probability and Terms||Basic probability concepts | Conditional probability | To use Bayes’ Theorem to revise probabilities | Various counting rules|
|Probability Distributions||Types of Distributions like Discrete and Continuous | Functions of Random Variables | Probability Distribution
|Sampling and Sampling Distributions||To distinguish between different sampling methods | The concept of the sampling distribution | To compute probabilities related to the sample mean and the sample proportion | The importance of the Central Limit Theorem|
|Confidence Interval Estimation||To construct and interpret confidence interval estimates for the mean and the proportion | How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion | How to use confidence interval estimates in auditing|
|Fundamentals of Hypothesis Testing||Null and Alternate hypothesis | One sample Tests | Test statistic and critical values | Possible errors in testing | p-value approach | t and z-tests | Testing on proportions|
DATA TRANSFORMATION AND VISUALIZATION
|Data Transformations||Merge, Rollup, Transpose and Append | Smoothing | Aggregation | Normalization | Attribute construction|
|Feature Engineering||Missing value Imputation | Outlier Analysis and Treatment | Binning | Creating dummy variables | feature scaling | Extracting Date | Log Transformation | Feature split | Label Encoding | One-Hot Encoding|
|Exploratory Data Analysis||Summarizing and Visualizing the Important Characteristics of Data | Visualization techniques – Box plot | Histogram | Multi-variate chart | Pareto chart | Scatter plot | Stem-and-leaf plot.|
|Introduction to Python||Installation | Python Basics | Spyder IDE | Jupyter Notebook | Floats and Strings Simple Input & Output | Variables | Operators | Single and Multiline Comments | Taking input from user|
|Data Structures||List | Strings | Tuple | Dictionary | Sets and their examples|
|Conditional Statements||If | if-else | if-elif-else | nested if else|
|Loops||For | while | nested loops| loop control statements|
|Functions||Creating user defined Functions | Function arguments like-Required, Keyword, Default and variable-length | Scope of variables in creating functions | Anonymous Functions – Lambda|
|Exception and File handling||Exception Handling | Raising Exceptions | Assertions | Files I/O|
|INTRODUCTION TO MACHINE LEARNING|
|Introduction||What is Machine Learning? | End-to-end Process of Investigating Data Through a Machine Learning Lens | Evolution and Trends | Application of Machine Learning | Best Practices of Machine Learning|
|Machine Learning Methods||Supervised | Unsupervised|
|Machine Learning Algorithms||Classification | Regression | Time Series | Collaborative Filtering | Clustering | Principal Component Analysis|
|MACHINE LEARNING WITH PYTHON|
|Numpy module||Basics of numpy | creating arrays | Numpy functions|
|Pandas||Introduction to Pandas | IO Tools | Pandas – Series and Dataframe and their functions|
|Matplotlib and Seaborn||Graphical representation of data using various plots|
|Scikit learn||Introduction to SciKit Learn | Load Data into Scikit Learn | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classification & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Decide What’s the Best Model for Every Scenario|
|MACHINE LEARNING APPLICATIONS|
|Linear Regression||Implementing Simple & Multiple Linear Regression | Making Sense of Result Parameters | Model Validation | Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity | Prediction and Confidence Intervals | Use Cases|
|Logistic Regression||Implementing Logistic Regression| Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures | Model Validation: Cross Validation, ROC Curve, Confusion Matrix | Use Cases|
|Decision Tree||Implementing Decision Trees | Homogeneity | Entropy Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Errors|
|Naïve Bayes||Bayes Theorem | Gaussian Naïve Bayes & its implementation | Multinomial Naïve Bayes, Count vectorizer, TF-IDF Vectorizer and its use cases on Text Classification|
|K-Nearest Neighbors||Concept of nearest neighbors | Euclidean Distance | Use Cases of KNN Classifier & Regressor|
|Support Vector Machine||Introduction to support vectors | Concept of hard & soft margins | slack variable | Lagrangian Primal & Dual | Kernel Trick | Use Cases|
|Time Series||Introduction | Components of Time Series | Stationarity | ACF | PACF | ARIMA model for forecasting | Use Cases|
|K-Means Clustering||Clustering concept | Finding optimal number of clusters | Use Cases|
|Hierarchical Clustering||Agglomerative & Divisive Clustering | Dendrograms | Linkage Matrix like Single, Complete & Average | Use Cases|
|Ensemble Techniques||Bagging | Boosting | Stacking | Regularization | Different cross-validation techniques used to treat Over fitting and Under fitting in machine learning models|
|Principal Component Analysis||Concept of Dimensionality reduction | Eigen values | Eigen
Vectors | Use Cases
|PROJECT 1||Title: Real Estate Price Prediction using Linear Regression
Industry: Real Estate
Description: The goal of this Use-case is to make property price predictions using Real Estate data. The dataset contains the of the price of apartments and various characteristics of the property. Based on this data, decide on the price of new properties.
|PROJECT 2||Title: Loan Prediction using Logistic Regression & Decision Tree
Description: The goal is to build a classification model to predict if a loan is approved or not. Dataset contains demographic information like age, income etc. of various customers. Based on this, predict for a new customer whether loan will be approved or not
|PROJECT 3||Title: Student’s Performance Prediction using KNN & Naïve Bayes
Description: The goal is to build a classification model to predict student’s performance from various characteristics associated with student’s academics
|PROJECT 4||Title: Handwritten digit recognition using Decision Tree &Random forest
Description: Dataset contains various pixel values of handwritten digits between 0 to 9. The goal is to build a classification model to predict the digit from its pixel values.
|PROJECT 5||Title: Amazon fine food review-Sentiment Analysis
Description: The analysis is to study Amazon food review from customers and try to predict whether a review is positive or negative. The dataset contains more than 500k reviews with number of upvotes and total votes to those comments
DEEP LEARNING APPLICATIONS
|Neural Networks||Understanding Neural Networks | The Biological Inspiration | Perceptron Learning & Binary Classification | Backpropagation Learning | Learning Feature Vectors for Words | Object Recognition|
|Keras||Keras for Classification and Regression in Typical Data Science Problems | Setting up KERAS | Di erent Layers in KERAS | Creating a Neural Network Training Models and Monitoring | Artificial Neural Networks|
|Tensorflow||Introducing Tensorflow | Neural Networks using Tensorflow | Debugging and Monitoring | Convolutional Neural Networks|
|Recurrent Neural Networks||Introduction to RNN | RNN long & short term dependencies | Vanishing gradient problem | Basic LSTM | Step by step walk through LSTM | Use cases|
ANN ON KERAS
|Title: Credit Default using ANN on Keras
Description: This research aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification – credible or not credible clients.
CNN ON KERAS & TENSORFLOW
|Title: Handwriting/Facial recognition using CNN on TensorFlow & Keras
Industry: Pattern Recognition
Description: This project will help build a model using Convolutional Neural Network to recognize handwriting/faces
|Title: Google stock price prediction
Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price
Computer Vision with Python:
- Introduction to OpenCV
- Core Operations
- Image Processing in OpenCV
- Feature Detection and Description
- Video Analysis
- Machine Learning
- Object Detection
- OpenCV-Python Bindings
NLP with Python:
- Introduction to NLTK
- Tokenizing words and Sentences with NLTK
- Stop words and Stemming with NLTK
- Part of Speech Tagging with NLTK
- Chunking & Chinking
- Lemmatizing with NLTK
- Wordnet with NLTK
- Converting words to Features
- Text Classification with NLTK
- Combining Algorithms with NLTK
- Creating a module for Sentiment Analysis
- Twitter Sentiment Analysis with NLTK
- Named Entity Recognition with Stanford NER Triggers
- Testing NLTK and Stanford NER Triggers for Accuracy and Speed
- Over view and basic syntax of R
- R variables and operators
- R Objects
- Data Frame
- Conditional statements of R
- If -else if-else
- Nested if-else
- Loops in R
- Repeat loop
- While loop
- For loop
- Loop control statements
- Creating user defined functions in R
- Data reshaping and File handling
- Reading excel, csv, xml files in R
- Reading Web data in R
- Connecting to Data bases in R
- Data manipulation with dplyr package(Case studies on data manipulation with real time datasets)
- Data visualization with ggplot package & case studies
- Creating Interactive Dash Boards with R markdown and Shiny
SQL (Structured Query Language)
- Introduction to SQL
- SQL Select Statements
- Execute a basic SELECT statement
- Restricting and Sorting Data
- Limit the rows retrieved by a query
- Sort the rows retrieved by a query
- Single-Row Functions
- Describe various types of functions available in SQL
- Use character, number, and date functions in SELECT statements
- Describe the use of conversion functions
- Displaying Data from Multiple Tables
- Write SELECT statements to access data from more than one table using equality and nonequality joins
- View data that generally does not meet a join condition by using outer joins
- Join a table to itself by using a self join
- Aggregating Data Using Group Functions
- Identify the available group functions
- Describe the use of group functions
- Group data using the GROUP BY clause
- Include or exclude grouped rows by using the HAVING clause
- Manipulating Data
- Describe each DML statement
- Insert rows into a table
- Update rows in a table
- Delete rows from a table
- Merge rows in a table
- Control transactions
- Creating and Managing Tables
- Including Constraints
- Describe constraints
- Create and maintain constraints
- Introduction to Tableau Software
- Tableau Worksheets
- Tableau Calculations
- Tableau sort and filter
- Tableau charts
- Tableau Advanced
- Case Study with Tableau
Call Our Career Advisor Now for Requisite Details.
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Frequently Asked Questions
We are Providing 3 Month and 6 Month Curriculums on Data Science includes 2 Real Time Projects, MNC certifications, Job support, Community Meetups, Bootcamps etc to Build your Career into data science field.
- IT Professionals,
- Data Analysts,
- Business Analysts,
- Functional Managers,
- Post Graduates
- Also, anyone having interest in Data Science
You need to get skilled on following, in order to take a deep dive in this field.
- Statistic and probability
- Programming Languages (Java, Scala ,SQL, R, Python)
- Data mining
- Machine learning
- Deep Learning
- Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
- 6,000 unique job postings for data scientists each week.
- 3,00,000+ Skilled Data Scientists required by the End of 2018.
- salaries range from Min 3L per Annum to 20 L per Annum.
- GlassDoor ranked it top place for popular jobs
Yes, We have a Dedicated HR team for Placement support Like Resume Preparations, Mock Interviews, Main Interviews, Company Tie-ups etc
We Provide Data Science Mastery Certificate from Social Prachar and Two More MNC certifications. Get In touch with the Team for more details about all our certifications.
Yes All the data science Trainees need to work on min 2 Real Time projects for Better understanding of Real Time Data Science Methods and Techniques.
Our Trainer Faculty is from Top MNC’s who has 10+ years of Experience with Real time clients. They will Mentor you all the way till you will get confidence on the subject. We have Community meetups,Boot Camps,workshops etc where you can meet the industry experts.
Data Science is the New Buzz word is the Market Right Now !
Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. A Data Scientist is someone who is better at Statistics than any software Engineer and better at Software Engineering than any Statistician. They are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals.
Yes, You are Eligible to Learn Data Science Course If you are Okay to Learn Programming and Good with Mathematics and Business Knowledge
We will Start from basics Like Introduction to Data science,R,Python,Deep Learning,Statistics,Machine Learning, Hadoop, Spark, SQL Etc with Two Real Time Projects at the End for Better Understanding of Data Science.
Yes, Undoubtedly Your Profile is Eligible to Learn Data Science course.
I'm from EEE background , can I go for data science , I don't have basics of any programming language
Yes, you can if you are willing to learn python programming and good with mathematics and statistics.
All Bsc, BCom, BTech, MBA, MTech MCA are eligible to learn Data Science.
Yes, Data Science ,R,Python,and tablueau, etc
Now its booming so anyone who completed Btech/MBA/Degree etc can learn, but only if you are willing to learn programming.
Yes you can opt but need to work a lot on maths. Our team will provide you the required material to overcome.
Yes, IF, You have less than 0-2 years in Pharmacy you can learn. And also if u are willing to learn programming you can learn data science.
I'm from mechanical background, but i want to learn data science , is it possible to understand without knowledge in languages like c , java knowledge
Yes, you can learn easily. Our curriculum starts from scratch, so you can learn without much confusions.
No. If its just for learning no problem. But if u need to make a career into data science, it will be little tough.
No Minimum criteria but need to have good knowledge on Mathematics.