Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Generator functions
• Lambda functions
• Map, reduce, filter functions
MODULE 5: PYTHON NUMPY PACKAGE
• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
MODULE 6: PYTHON PANDAS PACKAGE
• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA SCIENCE
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: R LANGUAGE ESSENTIALS
• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures , control statements
MODULE 6: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 7: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification, And Regression
• Supervised Vs Unsupervised
MODULE 2: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 4: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 7: ML ALGO: DECISION TREE
• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 9: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
MODULE 12: ADVANCED ML CONCEPTS
• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross-validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set: Smote Technique
• Feature Selection Techniques
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC
MODULE 2: FEATURE ENGINEERING
• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling
MODULE 3: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model Deployment
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker
MODULE 8: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING, AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
MODULE 5: UNDOING CHANGES
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 6: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2: HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language
MODULE 5: MACHINE LEARNING WITH SPARK ML
• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest
MODULE 6: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
MODULE 2: BI WITH TABLEAU: INTRODUCTION
• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE
• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
MODULE 4: TABLEAU: BUSINESS INSIGHTS
• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• The Formatting Pane
• Trend Lines & Reference Lines
MODULE 5: DASHBOARDS, STORIES AND PAGES
• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
MODULE 6: BI WITH POWER-BI
• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• import and export dataset
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management
MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
MODULE 2: DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE 3: TENSORFLOW FOUNDATION
• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow
MODULE 4: COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm
MODULE 6: AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
Course Covers Data Science Overview, Basics Of Statistics, Machine Learning, Use Cases
Course Covers Python/R, Statistics, Machine Learning Algorithms, Business Aspects And Tableau
Course Covers Essential Statistics For Data Science, Hypothesis Testing And Exploratory Analysis
Course Covers Python/R, Statistics, Machine Learning Algorithms, Business Aspects And Tableau.
The Course Covers Machine Learning, Deep Learning, AI, Big Data, AWS, Python/R, Statistics, Business Aspects & Tableau.
Career-oriented Course With The Industry-aligned Syllabus In Data Science.
A Data Science Specialization Course In The Operations Domain With In-depth Use Cases.
Specialized Course For Managers With Core Concepts Of Data Science For Data-driven Decisions.
A Data Science Specialization Course In The Marketing Domain With In-depth Use Cases.
A Data Science Specialization Course In The Human Resource Domain With In-depth Use Cases.
DataMites is a global institute for data science in Ludhiana which gives specialized coaching in various fields such as Data Science, Machine Learning, Deep Learning, Artificial Intelligence, the Internet of Things and Python Learning. We render higher knowledge achievement in the Data Science and Machine Learning domain in a cost-effective manner. The real-time training is highly qualitative for those who wish to enhance their knowledge on these subjects. The main goal of Datamites is to mould professionals who can bravely withstand the complexities of the analytical field. Indeed having a deep understanding of the new technologies can be rewarding in more ways than known.
“We’re entering a new world in which data may be more important than software.”
-Tim O’ Reilly (Founder, O’ Reilly Media)
The volume of data produced steadily is 1.145 trillion MB per day!
Companies manoeuvre data to lead and cultivate their everyday business. The intrinsic purpose of data science is to back up companies in making fast-track and superior decisions that can function to beat the pinnacle of their market. Subsequently, the number of companies using big data is escalating.
We provide 6 monthly Data Science Courses in Ludhiana within a three-phase learning method.
Phase 1 = It’s time to get prepared for the course to come, candidates would be provided with self-study videos and books of high quality to enable an excellent grasp of the curriculum as a whole.
Phase 2 = The primary stage of Live Intensive training along with hands-on capstone projects and after the training, you will receive IABAC Data Science Certification, a global certification.
Phase 3 = Projects, Internships and Job ready Program.
Courses we offer in Data Science:
1.Data Science Foundation - The Data Science Foundation course is a beginner’s course specifically designed to let the freshers kick off their career in Data Science. The course introduces Data Science, Data Science vs Business Analytics vs Big Data, Various Classifications of Business Analytics, Data Science Project Workflow, Roles in Data Science and Industry applications of Data Science.
2.Certified Data Scientist: The Certification Data Scientist course offers a wide-ranging idea of Data Science to those interested to dive into the Data Science domain. Our CDS Training is for 8 Months. DataMites has trained over 25,000 learners with the aid of our 50+ elite and professional faculties
The course is a complete pack with detailed learning of 15 courses:
* Data Science Foundation
* Python Essentials for Data Science
* R Language Essentials
* Math for Data Science
* Statistics for Data Science
* Data Preparation with Pandas
* Visualization with Python
* Machine Learning Associate
* Advanced Machine Learning
* SQL for Data Science
* Deep Learning – CNN Foundation
* Tableau Associate
* ML Model Deploy - Flask API
* Big Data Foundation
* Data Science Project Execution
Data Science Foundation - Introduction to Data Science, Industry applications, Terminologies
Python Essentials for Data Science - Anaconda - Python distribution Installation and setup. Jupyter Notebook, Python Basics, Data structures, control statements.
R Language Essentials - R Installation and Setup, R Studio Basics, R Data Structures, Control Statements, Data Science Packages.
Maths for Data Science - Essential mathematics, Linear algebra, Linear Transformation, Types of Matrices, Matrix properties and Operations, Probability and Calculus.
Statistics for Data Science - Run Down of Statistics, Terminologies, Inferential Statistics, Harnessing Data, Exploratory Analysis, Distributions, Central Limit Theorem, Hypothesis Testing, Correlation and Regression.
Data Preparation with Pandas - Numpy Array functions, Data munging with Pandas, imputation, outlier analysis.
Visualization with Python - Basics of Visualization, Matplotlib Introduction, Basic plots, customizing plots, Sub-plots, Statistical Plots, Seaborn package introduction.
Machine Learning Associate - Machine Learning Introduction, ML core concepts, Unsupervised and Supervised Learning, Clustering with K-Means, Linear Regression, Logistic Regression, K-nearest Neighbor.
Advanced Machine Learning - Bayes Theorem, Naïve Bayes Algorithm for text classification, Decision Tree, Ensemble methods: Random Forest, Extra Trees, SVM, Boosting Techniques, xgboost, Artificial Neural Network, Adv Metrics, Imbalanced Dataset, Grid search, K-fold Cross-validation
SQL for Data Science - Relational Database Management Systems basics, SQL Introduction, Connection to SQL databases, Fetching data with SELECT, WHERE condition, SQL JOINs, SQL CRUD operations.
Deep Learning - CNN Foundation - Deep Learning Introduction, TensorFlow and Keras, Convolution Neural Network basics, End to End image classification of cats and dogs using TensorFlow-Keras platform.
Tableau Associate - Visual Analytics basics, Tableau Introduction, Connecting to the data source, dimensions vs measures, basic plots, compound plots, forecasting, publishing.
ML Model Deploy - Flask API- ML Deployment Strategies, Flask introduction, Packing training ML model, Deploying it on Flask as API.
Big Data Foundation - Introduction to Big Data, Hadoop Concepts, Spark Big Data for Data Science Processing. Handling Big Data in Machine Learning Pipeline.
Data Science Project Execution - Data Science Project Management method, Business Case, Risk and Limitation of Machine Learning, Project Pitfalls.
Data Science Specialization Courses
Data Science for Managers is a significant program offered by DataMites specifically designed for those in managerial positions about the copious tools and techniques used to derive legitimate insights . A 2 week program imparting significant knowledge on Data Science Foundation, Statistics for Data Science, Building and Optimising Courses, Predictive Analysis and Regression, Decision Coursing and Visual Analytics and Tableau. Data Science knowledge and skills are quintessential for Managers of every organization, in effectively and efficiently making data-driven decisions.
Data Science for Finance - Investing in data science within finance includes a wide range of career opportunities. Areas with a technology focus include cyber security, data science, machine learning, and AI, among many others. Roles that necessitates financial or investment expertise are namely blockchain development and quantitative investing.
Data Science for Operations - Through data science, we can collect useful information and uncover insights that can be used to understand an organization's business and make data-driven decisions.
Data Science for Marketing - Data science is largely enforced in marketing areas like profiling, search engine optimization, responsiveness, customer engagement as well as real-time marketing campaigns. Furthermore, new ways to employ data science and analytics in marketing pop up every day.
Data Science for Finance - Data science within finance encloses an extensive variety of opportunities for investment careers. Data Science enables better risk management and risk analysis. Better analysis can contribute to superior decisions which would shoot up the profit for financial institutions.
Data Science for HR - Having a data science professional in the HR team can render the organization with insights for soundly managing employees so that business goals can be reached steadily and expeditiously. Data science is a radical method that enables companies to quantitatively monitor the expenses, the result of candidate engagement projects and to analyse the cost-effectiveness of different HR activities.
Data Science with R - 2 month training giving comprehensive training on R, python, statistics, Machine learning algorithms, Business Aspects and Tableau. DataMites Data Science with R training, lets you master data manipulation with R Programming, data visualization, advanced analytics topics like regression and data mining using RStudio.
Statistics for Data Science - Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can search for meaningful trends and changes in Data. In simple words, Statistics can be employed to gather meaningful insights from data by performing mathematical computations on it.
Python for Data Science - Python being one of the best languages used by data scientists for various data science projects/applications supplies great functionality to deal with mathematics, statistics and scientific function. Our syllabus encompasses introduction to data science with Python, Python Basics, Numpy Package, Pandas Package, Python Advanced: Data Munging with Pandas, Python Advanced: Visualization with MatPlotLib, Exploratory Data Analysis: Data Cleaning, Data Wrangling Exploratory Data Analysis: Case Study.
Datamites offers flexible learning options starting from Live Online Training, Exclusive Data Science Classroom Training on demand and/or Exceptional Recorded Sessions. As per a LinkedIn report, data science prevails to dominate its arising job rankings with a massive 37% growth in the last three years.
DataMites Data Science course fee ranges from 88,000 INR to 110,000 INR as per your preferred choice of learning. You can always check in on your desired course and find out the course fee for the same.
Global Recognition - IABAC Accreditation
Expert Training - Elite Faculty with relevant research and coaching experience
Real-time Projects and Internships
100% Job Ready Assistance
Ludhiana, a large industrial city in Punjab is resting on the banks of river Sutlej. Hailed as the 'Manchester of India', Ludhiana is also an attractive manufacturing hub in India.
The present time business marketplace has constructed a data-driven environment. The role of data is to equip business leaders to assemble decisions in regards to the facts, trends and statistical numbers. Data is at the crux of pretty much every business decision being made. By efficiently manipulating data, a company can manage and streamline the process of producing a product and distributing it to the customer.
Data Science Courses in Ludhiana are the opening move to be thriving in the IT systems in the city; Becoming a Data Scientist was never this easy? Join Data Science Training in Ludhiana and Get yourself upskilled for a dynamic career path!
Did You Know?
The Average Data Scientist Salary in India is INR 8,24,972 per annum. Average Data Scientist Salary in Ludhiana as reported by Indeed.com - INR 3,78,117 annually. Data Science Certification in Ludhiana can help in indisputably forging ahead in the data science domain!
Data can be vast and distorted and converted into valuable information. Data science entails mining large datasets consisting of structured and unstructured data and identifying hidden patterns to extract actionable insights.
Data is nothing without science
Better customer experience
Increase job opportunities
Rising salary for data science professionals
You will have many job titles to choose from
You will play a pivotal part in decision making in the company
Anyone, whether a newcomer or a professional, interested in learning Data Science can opt for it. Engineers, marketing professionals, software and IT professionals can go after part-time or external programs in data science. Basic high school level subjects are the minimum requirement for regular data science courses.
Data science course fees will vary according to the level of training you are looking for. However, when we discuss the fee structure, whether you choose any training provider for your classroom training for Data Science, it ranges from INR 30,000 to INR 1,00,000.
Machine learning engineer
Business intelligence analyst
Skills such as data analysis, statistical knowledge, data storytelling, communication and problem-solving will be beneficial for learning data science.
Knowledge of Python, R, Excel, C++, Java and SQL is always preferred. But you can always learn from the fundamentals and improve yourself.
Like any other field, with proper guidance Data Science can become an easy field to learn, and one can make a career in this field. However, since it is huge, it is easy for beginners to get lost and lose sight, making the learning experience difficult and frustrating.
Some of the major Data Science Tools include; SAS, Apache Hadoop, Tableau, BigML, BigML, Knime, RapidMiner, Excel, Apache Flink, Power BI.
According to IDC, global data will increase to 175 zettabytes by 2025. Data Science facilitates companies to productively comprehend and maneuver vast data from multiple sources and obtain worthy insights to make better data-driven decisions. Data Science is profusely used in countless industry domains like marketing, healthcare, finance, banking and policy work to name a few. The significance of data science is henceforth evident.
Yes, companies hire freshers for Data Scientist posts. Indeed, most entry-level analytics jobs in India do not call for any specialization or post-graduation. The only qualification you need in these companies is an engineering degree and even the stream doesn't matter. These companies only look for your Aptitude, Communication Skills and Critical Reasoning.
Data Science is wide-ranging and its applications are infinite. Companies all over the world are searching for data science professionals who can be an asset to their companies. Data science certifications can be valuable for your career ahead in this technology-driven world.
66% of data scientists proclaimed in 2018 that they used Python every single day, making Python the number one language for data science. To successfully undertake data science work, having knowledge and expertise in Python or any other programming language is mandatory.
Yes, statistics is the soul of data science and is indispensable for achieving any machine learning algorithm. Statistics make it easy to operate on data. Various statistical techniques like classification, regression, hypothesis testing, time series analysis are used to build data models. With the help of statistics, a data scientist can gain better insights, which enables the decision-making process to be streamlined effectively.
The duration of the Data Science course in Ludhiana is 8 months, totalling 120 hours of training. Training sessions are imparted on weekdays and weekends. You can choose any as per your availability.
Data Science is a highly sought after field of study that assures highly lucrative salary packages. Aspirants can enrol at Datamites for Data Science Course in Ludhiana, we provide in-depth training for your further career.
No, a PG degree is not necessary but having prior knowledge of Mathematics, Statistics, Economics or Computer Science can be highly beneficial.
Being the IT hub of India, Ludhiana has an assortment of opportunities for both freshers and experienced. Data Science certification can only help you in myriad ways.
A certified data scientist is a person who has acquired complete knowledge of the data science domain. The CDS course is specially designed for those who wish to enter the Data Science domain - fresh and with the best skills and guidance to succeed in the domain.
CDS course is designed for data science freshers who want to mark their grades and conquer the world of Data Science.
DataMites offers Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Statistics for Data Science, Data Science Marketing, Data Science Operations, Data Science Retail, Data Science for HR, Data Science with Finance and Data Science.
Datamites™ is the global institute for data science accredited by the International Association of Business Analytics Certification (IABAC).
We have more than 25,000 students enrolled in the courses we offer.
We provide a three-step learning method. In Phase 1, self-study videos and books will be provided to the candidates to help them get adequate knowledge about the syllabus. Phase 2 is the primary phase of intensive live online training. And in the third phase, we will release the projects and placements.
The entire training includes real-world projects and highly valuable case studies.
After the training, you will receive the IABAC certification which is a global certification.
After completing your training, you will get the chance to do an internship with AI company Rubix, a global technology company.
The fees for the Data Science course will range from Rs.6000 to Rs.88,000 depending on the course and mode of training you choose.
We offer you flexible learning options ranging from live online, self-learning methods to classroom training. You can choose as per your liking.
At present DataMites offers only online training in Ludhiana. But we are on board to conduct classroom training ON-DEMAND by the candidates and by checking the number of demands for the same.
We are determined to provide you with trainers who are certified and highly qualified with decades of experience in the industry and well versed in the subject matter.
Our Flexi-Pass for Data Science training will allow you to attend sessions from Datamites for a period of 3 months related to any query or revision you wish to clear.
We will issue you an IABAC® certification that provides global recognition of relevant skills.
Of course, after your course is completed, we will issue you a Course Completion Certificate.
Yes. Photo ID proofs like a National ID card, Driving license etc. are needed for issuing the participation certificate and booking the certification exam as required.
You don't need to worry about it. Just get in touch with your instructors regarding the same and schedule a class as per your schedule.
In the case of online training, each session will be recorded and uploaded so that you can easily learn what you missed at your own pace and comfort.
Yes, a free demo class will be provided to you to give you a brief idea of how the training will be done and what will be involved in the training.
Yes, we have a dedicated Placement Assistance Team (PAT) who will provide you with placement facilities after the completion of the course.
Learning Through Case Study Approach
Theory → Hands-on → Case Study → Project → Model Deployment
Yes, of course, it is important that you make the most of your training sessions. You can of course ask for a support session if you need any further clarification.
We accept payment through;
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: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.
IABAC Global Certifications
Comprehensive : Math, Stats, Machine Learning, Python, R, Tableau
8-month | 700 Learning Hours
Internship | Job Assistance
IABAC Global Certifications
Comprehensive: Computer vision, NLP,
Deep Learning, Reinforcement Learning
11-Month | 780 Learning Hours
Internship | Job assistance