Data Science Masters Program Training Course
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This Data Science Training course, in collaboration with IBM, options exclusive IBM hackathons, masterclass, and Ask-me-anything sessions for the best training expertise. This data scientist certification training provides active exposure to key technologies together with R, Python, Machine Learning, Tableau, Hadoop, and Spark via live interaction with practitioners, sensible labs, and trade comes.
- 40+ Hrs Hands On Training
- 2 Live Projects For Hands-On Learning
- 50 Hrs Practical Assignments
- 24/7 Students
- Exclusive Hackathons and Live interaction with Certified expertIncludes live Master Classes and Ask me anything sessions
- 250+ hours of live interactive learningLive Online classes by industry experts
- Capstone and 12+ real-life projectsBuilt on Segmentation project, Trip History dataset and etc.
- LearnoVita Job Assist™Get noticed by the top hiring companies
- Course 1 Data Science With R Program
- Course 2 Data Science with Python
- Course 3 Machine Learning
- Course 4 Tableau Training
- Course 5 Big Data Hadoop and Spark Developer
- Course 6 Data Science Capstone
For Business
Customized learning paths, 4x outcomes & completion rates; award-winning client support.
Online Classroom Batches Preferred
16-Sep-2024
Monday (Monday - Friday)
Weekdays Regular
08:00 AM (IST)
(Class 1Hr - 1:30Hrs) / Per Session
11-Sep-2024
Wednesday (Monday - Friday)
Weekdays Regular
08:00 AM (IST)
(Class 1Hr - 1:30Hrs) / Per Session
14-Sep-2024
Saturday (Saturday - Sunday)
Weekend Regular
11:00 AM (IST)
(Class 3hr - 3:30Hrs) / Per Session
14-Sep-2024
Saturday (Saturday - Sunday)
Weekend Fasttrack
11:00 AM (IST)
(Class 4:30Hr - 5:00Hrs) / Per Session
Can't find a batch you were looking for?
₹124000
₹62000
10% OFF Expires in
No Interest Financing start at ₹ 5000 / month
Skills You Will Gain
- Core JSP and Servlets
- J2EE , Struts, Spring
- Hibernate, JDBC, Web Services
- Advanced JSP and Servlets
- EJB, JDO, JSF
- Android Development
- Servlets
Android App Developer Course Key Features 100% Money Back Guarantee
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5 Weeks Training
For Become a Expert -
Certificate of Training
From Industry Android App Developer Experts -
Beginner Friendly
No Prior Knowledge Required -
Build 3+ Projects
For Hands-on Practices -
Lifetime Access
To Self-placed Learning -
Placement Assistance
To Build Your Career
Top Companies Placement
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Annual SalaryHiring Companies
Data Science Master Program Course Curriculam
Trainers Profile
Trainers are certified professionals with 13+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Data Science Master Program Course Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Syllabus of Data Science Master Program Course Online Course Download syllabus
- Data Types
- Introduction to Data Science Tools
- Statistics
- Approach to Business Problems
- Numerical Categorical
- R, Python, WEKA, RapidMiner
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- Introduction to Correlation Spearman Rank Correlation
- OLS Regression – Simple and Multiple Dummy variables
- Multiple regression
- Assumptions violation – MLE estimates
- Using UCI ML repository dataset or Built-in R dataset
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- Data preparation & Variable identification
- Advanced regression
- Parameter Estimation / Interpretation
- Robust Regression
- Accuracy in Parameter Estimation
- Using UCI ML repository dataset or Built-in R dataset
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- Introduction to Logistic Regression
- Logit Function
- Training-Validation approach
- Lift charts
- Decile Analysis
- Using UCI ML repository dataset or Built-in R dataset
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- Introduction to Cluster Techniques
- Distance Methodologies
- Hierarchical and Non-Hierarchical Procedure
- K-Means clustering
- Introduction to decision trees/segmentation with Case Study
- Using UCI ML repository dataset or Built-in R dataset
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- Introduction to Time Series
- Data and Analysis
- Decomposition of Time Series
- Trend and Seasonality detection and forecasting
- Exponential Smoothing
- Building R Dataset
- Sales forecasting Case Study
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- Box – Jenkins Methodology
- Introduction to Auto Regression and Moving Averages, ACF, PACF
- Detecting order of ARIMA processes
- Seasonal ARIMA Models (P,D,Q)(p,d,q)
- Introduction to Multivariate Time-series Analysis
- Using built-in R datasets
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- Live example/ live project
- Using client given stock prices / taking stock price data
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- Box – Jenkins Methodology
- Case Study with the Data
- Based on open set data
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- Case Study with the Data
- Based on open set data
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- Supervised Learning Techniques
- Conceptual Overview
- Unsupervised Learning Techniques
- Association Rule Mining Segmentation
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- Fraud Identification Process in Parts procuring
- Sample data from online
- Text Analytics
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- Sample text from online
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- Social Media Analytics
- Sample text from online
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- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
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- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
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- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
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- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
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- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
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- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
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- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Probability Basics
- What does mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Variance
- Bias variance Trade off
- Underfitting
- Overfitting
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- catter plot
- Cook’s Distance
- Missing Value treatments
- What is a NA?
- Central Imputation
- KNN imputation
- Dummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
- Error Metrics
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
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- Linear Regression
- Linear Equation
- Slope<
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff
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- K-Means
- K-Means ++
- Hierarchical Clustering
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- Title
- Base
- Link
- Style s
- Script
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- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
- Learn the use of IDE R Studio
- Use the ‘R help’ feature in R
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- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
- Factors
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- Data sorting
- Find and remove duplicates record
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Merging Data
- Apply functions
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- Reading Data
- Writing Data
- Basic SQL queries in R
- Web Scraping
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- Box plot
- Histogram
- Pareto charts
- Pie graph
- Line chart
- Scatterplot
- Developing Graphs
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- Basics of Statistics
- Inferencial statistics
- Probability
- Hypothesis
- Standard deviation
- Outliers
- Correlation
- Linear & Logistic Regression
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- Introduction to Data Mining
- Understanding Machine Learning
- Supervised and Unsupervised Machine Learning Algorithms
- K- means clustering
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- Anova
- Sentiment Analysis
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- Decision Tree
- Concepts of Random Forest
- Working of Random Forest
- Features of Random Forest
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- Start Page
- Show Me
- Connecting to Excel Files
- Connecting to Text Files
- Connect to Microsoft SQL Server
- Connecting to Microsoft Analysis Services
- Creating and Removing Hierarchies
- Bins
- Joining Tables
- Data Blending
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- Parameters
- Grouping Example 1
- Grouping Example 2
- Edit Groups
- Set
- Combined Sets
- Creating a First Report
- Data Labels
- Create Folders
- Sorting Data
- Add Totals, Sub Totals and Grand Totals to Report
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- Area Chart
- Bar Chart
- Box Plot
- Bubble Chart
- Bump Chart
- Bullet Graph
- Circle Views
- Dual Combination Chart
- Dual Lines Chart
- Funnel Chart
- Traditional Funnel Charts
- Gantt Chart
- Grouped Bar or Side by Side Bars Chart
- Heatmap
- Highlight Table
- Histogram
- Cumulative Histogram
- Line Chart
- Lollipop Chart
- Pareto Chart
- Pie Chart
- Scatter Plot
- Stacked Bar Chart
- Text Label
- Tree Map
- Word Cloud
- Waterfall Chart
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- Dual Axis Reports
- Blended Axis
- Individual Axis
- Add Reference Lines
- Reference Bands
- Reference Distributions
- Basic Maps
- Symbol Map
- Use Google Maps
- Mapbox Maps as a Background Map
- WMS Server Map as a Background Map
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- Calculated Fields
- Basic Approach to Calculate Rank
- Advanced Approach to Calculate Ra
- Calculating Running Total
- Filters Introduction
- Quick Filters
- Filters on Dimensions
- Conditional Filters
- Top and Bottom Filters
- Filters on Measures
- Context Filters
- Slicing Fliters
- Data Source Filters
- Extract Filters
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- Tableau online.
- Overview of Tableau Server.
- Publishing Tableau objects and scheduling/subscription.
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- Necessity of Big Data and Hadoop in the industry
- Paradigm shift - why the industry is shifting to Big Data tools
- Different dimensions of Big Data
- Data explosion in the Big Data industry
- Various implementations of Big Data
- Different technologies to handle Big Data
- Traditional systems and associated problems
- Future of Big Data in the IT industry
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- Why Hadoop is at the heart of every Big Data solution
- Introduction to the Big Data Hadoop framework
- Hadoop architecture and design principles
- Ingredients of Hadoop
- Hadoop charLearnoVitaristics and data-flow
- Components of the Hadoop ecosystem
- Hadoop Flavors – Apache, Cloudera, Hortonworks, and more
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- Hadoop environment setup and pre-requisites
- Hadoop Installation and configuration
- Working with Hadoop in pseudo-distributed mode
- Troubleshooting encountered problems
- Hadoop environment setup on the cloud (Amazon cloud)
- Installation of Hadoop pre-requisites on all nodes
- Configuration of masters and slaves on the cluster
- Playing with Hadoop in distributed mode
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- The need for a distributed processing framework
- Issues before MapReduce and its evolution
- List processing concepts
- Components of MapReduce – Mapper and Reducer
- MapReduce terminologies- keys, values, lists, and more
- Hadoop MapReduce execution flow
- Mapping and reducing data based on keys
- MapReduce word-count example to understand the flow
- Execution of Map and Reduce together
- Controlling the flow of mappers and reducers
- Optimization of MapReduce Jobs
- Fault-tolerance and data locality
- Working with map-only jobs
- Introduction to Combiners in MapReduce
- How MR jobs can be optimized using combiners
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- Anatomy of MapReduce
- Hadoop MapReduce data types
- Developing custom data types using Writable & WritableComparable
- InputFormats in MapReduce
- InputSplit as a unit of work
- How Partitioners partition data
- Customization of RecordReader
- Moving data from mapper to reducer – shuffling & sorting
- Distributed cache and job chaining
- Different Hadoop case-studies to customize each component
- Job scheduling in MapReduce
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- The need for an adhoc SQL based solution – Apache Hive
- Introduction to and architecture of Hadoop Hive
- Playing with the Hive shell and running HQL queries
- Hive DDL and DML operations
- Hive execution flow
- Schema design and other Hive operations
- Schema-on-Read vs Schema-on-Write in Hive
- Meta-store management and the need for RDBMS
- Limitations of the default meta-store
- Using SerDe to handle different types of data
- Optimization of performance using partitioning
- Different Hive applications and use cases
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- The need for a high level query language - Apache Pig
- How Pig complements Hadoop with a scripting language
- What is Pig
- Pig execution flow
- Different Pig operations like filter and join
- Compilation of Pig code into MapReduce
- Comparison - Pig vs MapReduce
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- NoSQL databases and their need in the industry
- Introduction to Apache HBase
- Internals of the HBase architecture
- The HBase Master and Slave Model
- Column-oriented, 3-dimensional, schema-less datastores
- Data modeling in Hadoop HBase
- Storing multiple versions of data
- Data high-availability and reliability
- Comparison - HBase vs HDFS
- Comparison - HBase vs RDBMS
- Data access mechanisms
- Work with HBase using the shell
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- The need for Apache Sqoop
- Introduction and working of Sqoop
- Importing data from RDBMS to HDFS
- Exporting data to RDBMS from HDFS
- Conversion of data import/export queries into MapReduce jobs
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- What is Apache Flume
- Flume architecture and aggregation flow
- Understanding Flume components like data Sources and Sinks
- Flume channels to buffer events
- Reliable & scalable data collection tools
- Aggregating streams using Fan-in
- Separating streams using Fan-out
- Internals of the agent architecture
- Production architecture of Flume
- Collecting data from different sources to Hadoop HDFS
- Multi-tier Flume flow for collection of volumes of data using AVRO
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- The need for and the evolution of YARN
- YARN and its eco-system
- YARN daemon architecture
- Master of YARN – Resource Manager
- Slave of YARN – Node Manager
- Requesting resources from the application master
- Dynamic slots (containers)
- Application execution flow
- MapReduce version 2 application over Yarn
- Hadoop Federation and Namenode HA
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- Introducing Scala
- Installation and configuration of Scala
- Developing, debugging, and running basic Scala programs
- Various Scala operations
- Functions and procedures in Scala
- Scala APIs for common operations
- Loops and collections- Array, Map, List, Tuple
- Pattern-matching and Regex
- Eclipse with Scala plugin
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- Introduction to OOP - object oriented programming
- Different oops concepts
- Constructors, getters, setters, singletons; overloading and overriding
- Nested Classes and visibility Rules
- Functional Structures
- Functional programming constructs
- Call by Name, Call by Value
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- Problems with older Big Data solutions
- Batch vs Real-time vs in-Memory processing
- Limitations of MapReduce
- Apache Storm introduction and its limitations
- Need for Apache Spark
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- Introduction to Apache Spark
- Architecture and design principles of Apache Spark
- Spark features and charLearnoVitaristics
- Apache Spark Ecosystem components and their insights
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- Spark environment setup
- Installing and configuring prerequisites
- Installation of Spark in local mode
- Troubleshooting encountered problems
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- Spark installation and configuration in standalone mode
- Installation and configuration of Spark in YARN mode
- Installation and configuration of Spark on a real cluster
- Best practices for Spark deployment
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- Working on the Spark shell
- Executing Scala and Java statements in the shell
- Understanding SparkContext and the driver
- Reading data from local file-system and HDFS
- Caching data in memory for further use
- Distributed persistence
- Spark streaming
- Testing and troubleshooting
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- Introduction to Spark RDDs
- How RDDs make Spark a feature rich framework
- Transformations in Spark RDDs
- Spark RDDs action and persistence
- Lazy operations and fault tolerance in Spark
- Loading data and how to create RDD in Spark
- Persisting RDD in memory or disk
- Pairing operations and key-value in Spark
- Hadoop integration with Spark
- Apache Spark practicals and workshops
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- The need for stream analytics
- Comparison with Storm and S4
- Real-time data processing using streaming
- Fault tolerance and checkpointing in Spark
- Stateful Stream Processing
- DStream and window operations in Spark
- Spark Stream execution flow
- Connection to various source systems
- Performance optimizations in Spark
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- Introducing Scala
- Installation and configuration of Scala
- Developing, debugging, and running basic Scala programs
- Various Scala operations
- Functions and procedures in Scala
- Scala APIs for common operations
- Loops and collections- Array, Map, List, Tuple
- Pattern-matching and Regex
- Eclipse with Scala plugin
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- Introduction to Spark SQL
- Apache Spark SQL Features and Data flow
- Architecture and components of Spark SQL
- Hive and Spark together
- Data frames and loading data
- Hive Queries through Spark
- Various Spark DDL and DML operations
- Performance tuning in Spark
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- Ignite Talk
- Statement of work
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- Milestone #1 Presentation
- Summary Report + technical report
- Self-/peer- evaluation
- Review another group's reports
- Code (runs as advertised)
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- Milestone #2 Presentation ("Midterm")
- Summary Report + technical report
- Self-/peer- evaluation
- Review another group's reports
- Code (runs as advertised)
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- Milestone #3 Presentation
- Summary Report + technical report
- Self-/peer- evaluation
- Review another group's reports
- Code (runs as advertised)
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- Final Presentation to class
- Final write-up via blog
- Poster and video recording
- Self-/peer- evaluation
- Code (runs, is organized and readable)
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Android App Developer Training Objectives
- Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.
- Data science uses complex machine learning algorithms to build predictive model
- Data Science Specialization
- Introduction to Data Science
- Applied Data Science with Python Specialization
- Data Science MicroMasters
- Dataquest.
- Statistics and Data Science MicroMasters
- The candidate must hold a BCA/ B.Sc Statistics / B.Sc Mathematics / B. Sc Computer Science / B.Sc IT./ BE or BTech or equivalent degree from a recognized university. He/ She must have scored 50% marks in the qualifying examination
- A Data Science course syllabus consists of four major subject matters – Foundation blocks, Machine Learning, Text Mining and Natural language Processing, and Big Data Analytics.
- Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year.
- This makes Data Science a highly lucrative career option.
- The answer is yes. A fresher can become a data analyst if he/she learns the tricks of the trade and work on honing the required skills.
- To get on the right track, freshers need to strategize on how they can outshine in the field and keep pace with those who already have relevant experience in the area.
- Learning data science is not easy.
- It will take a lot of work, a lot of energy and a lot of time from you.
- Business Intelligence Analyst.
- Data Mining Engineer.
- Data Architect.
- Data Scientist.
- Senior Data Scientist.
- Becoming a data scientist without a Master's degree or Doctorate degree is both possible and, frankly, not entirely rare. As we mentioned earlier. more than 25% of professional data scientists do not have a Master's or Doctorate.
- It is possible to learn Data Science with Low-Code experience.
- There are some basic principles of data science that you need to learn before learning Python, and you can start solving many real world problems without any coding at all!.
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Honestly Yes, We Provide 1 Set of Practice test as part of Your Android App Developer Training course. It helps you to prepare for the actual Android App Developer Certification exam. You can try this free Android App Developer Fundamentals Practice Test to Understand the Various type of tests that are Comes Under the Parts of Course Curriculum at LearnoVita.
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We at LearnoVita believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics and Can Access more information and Richer Understanding through teacher and other students' body language and voice. Therefore, we restrict the size of each Android App Developer batch to 5 or 6 members
Learning Android App Developer can help open up many opportunities for your career. It is a GREAT SKILL-SET to have as many developer roles in the job market requires proficiency in Android App Developer. Mastering Android App Developer can help you get started with your career in IT. Companies like Oracle, IBM, Wipro, HP, HCL, DELL, Bosch, Capgemini, Accenture, Mphasis, Paypal, and MindLabs.
The Average Android App Developer Developer salary in India is ₹4,43,568 per annum.
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