Keywords
Apache
Python
Big Data
Analysis
statistical analysis
Database Design
Data Warehousing
Data Warehouse
Business Intelligence
Microstrategy
Informatica
Talend
Tableau
Qlikview
power bi
Data Science
Machine Learning
neural networks
Reinforcement plans
Algorithm development
Apache Spark
Data Integration
etl
Cassandra
apache hbase
Time Series Data Bases
Classification
regression
Cluster
Predictive Analysis
Data Modeling
Skills
-Big Data -Analytics -Deep Learning -NLP -Python -Spark -TensorFlow -BI
Short Bio
Be extremely clear with your requirements and 5Es are sufficient to shortlist a service provider. Rest are stories.
EDUCATION: Indian Institute of Technology, IIT. One of the top technical universities worldwide.
EXPERIENCE: 12 Years. Hands-on experience working on challenging problems on data-to-decision.
EXPERTISE: Data-to-Decision. Big Data, Data Science, AI, Machine Learning, Analytics, BI, Web.
EXPOSURE: Served 75 large clients. Marketing, Sales, Supply, Growth, Performance - All industries.
EMPATHY: Make your life easy. Work as individual and team (15 fulltime experts) with commitments.
Seeing is Believing: https://github[.]com/h2hdata
Long Bio
Pranav is a data person helping enterprise, small business, and startups solving their data problems at scale. Graduate from Indian Institute of Technology, IIT Guwhati, carries 12 years of significant experience working on data engineering and data science. Pranav had been associated with esteemed businesses including Invitation Homes, Kaiser Permanente, Zurich Financial Services, Hughes Depot Supply, Boeing, Nielsen, Adap TV, Ecolab, CNA Financial, to name very few in list.
OFFERING
1. Data Ingestion
- Ingesting data from variety of sources to a big data ecosystem.
- Data sources can be operational systems (CRM, ERP, SCM, CMS), Databases (Relational, NO SQL), Streaming (Machine Logs, Social Media, Event Processing), Files (Word, Excel, CSV, PDF, Text)
- Big Data ecosystem can be Apache Hadoop or Apache Spark
2. Data Scale
- Moving data to a big data ecosystem
- Parallel processing through Apache Spark
- Storing data to NO SQL databases for pre-processing
3. Data Wrangling
- Data Lake development including data pipelines
- Pre-processing tasks including data parsing, data profiling, data shaping, data inferencing, data enrichment, data harmonization, data cleansing, data transformation, data combining
4. Data Context
- Natural Language Processing of un-structured and multi-structured data Entity selection, Featurization, Part-of-Speech, Lemmatization, Stemming, TF- IDF, Scoring - Entity Resolution, Linked Data, Semantic Web, Third Party enrichment, Ontology, knowledge graph, Word Disambiguation
5. Data Exploration
- Exploratory data analysis to summarize dataset characteristics
- Mining the hidden patterns in data
- Statistical analysis concluding right analytical model
6. Data Analytics
- Build rule based analytics model meeting requirements
- Build a salable machine learning model for large data
- Build a deep learning model for complex unstructured data
- Models based on algorithms including hyper-parameter optimization for regression, classification, clustering, association, neural networks, and deep learning - Validating and Testing the data model prepared
7. Data Communication
- Analyzing results via Visualization, D3.JS or Tableau
- Providing actionable insights through automation
SKILLS
1. Framework: Apache Hadoop (MapReduce, YARN, HDFS)
2. Distributed Programming: Apache Spark
- Parallel processing of large scale data, batch or streaming
- In-memory computation of big data in seconds or less
- Machine learning implementation - Graph data analysis
- Real-time streaming - Processing in Python, Scala, and R
3. Machine Learning: Python, PySpark, TensorFlow
4. Key Map Data Mode: Apache HBase, Apache Cassandra
5. Key Value Data: Reddis
6. Graph Database: Apache Giraph, Neo4J
7. Document Data Model: MongoDB
8. Data Ingestion: Apache Flume, Apache Sqoop
9. SQL-like Processing: Apache Hive, Apache Impala
10. Message Oriented Middleware: Apache Kafka, RabbitMQ
11. Service Programming: Apache Avro, Apache Zookeer
12. Scheduling: Apache Oozie
13. Applications: Apache Nutch, Apache Tika
14. Search Engine Framework: Apache Solr, Apache Lucene
15. Data Visualization: D3.JS, Tableau, Qlikview, MicroStrategy
16. Distribution: Cloudera, HortonWorks, MapR, Pivotal
17. Deployment: Amazon AWS, Microsoft Azure, Google Cloud, IBM Bluemix
18. Web: Flask, Django, Angular.JS, Ember.JS, Node.JS
SPECIALITY
1. Data Handling data from marketing automation, CRM, POS, click-through, social media, machines, third party
2. Analytics
- Customer
- Finance
- Supply
- Inventory
- HR
- IoT
3. Application Data Lake, DMP, Dashboards, Apps
Feedback
I worked with 75+ global clients for last 12 years and received an overall 8.75/10 rating (weighted average)
Communication
Possess excellent English skills. Use Skype to communicate with my clients.
Availability
Available from 9:30 AM (IST) to 1:00 AM (IST) for my clients from different time zones.
Contact
Please send your queries to pranav.verma.info[at]gmail.com or Skype at vpranav121
Short Bio
Be extremely clear with your requirements and 5Es are sufficient to shortlist a service provider. Rest are stories.
EDUCATION: Indian Institute of Technology, IIT. One of the top technical universities worldwide.
EXPERIENCE: 12 Years. Hands-on experience working on challenging problems on data-to-decision.
EXPERTISE: Data-to-Decision. Big Data, Data Science, AI, Machine Learning, Analytics, BI, Web.
EXPOSURE: Served 75 large clients. Marketing, Sales, Supply, Growth, Performance - All industries.
EMPATHY: Make your life easy. Work as individual and team (15 fulltime experts) with commitments.
Seeing is Believing: https://github[.]com/h2hdata
Long Bio
Pranav is a data person helping enterprise, small business, and startups solving their data problems at scale. Graduate from Indian Institute of Technology, IIT Guwhati, carries 12 years of significant experience working on data engineering and data science. Pranav had been associated with esteemed businesses including Invitation Homes, Kaiser Permanente, Zurich Financial Services, Hughes Depot Supply, Boeing, Nielsen, Adap TV, Ecolab, CNA Financial, to name very few in list.
OFFERING
1. Data Ingestion
- Ingesting data from variety of sources to a big data ecosystem.
- Data sources can be operational systems (CRM, ERP, SCM, CMS), Databases (Relational, NO SQL), Streaming (Machine Logs, Social Media, Event Processing), Files (Word, Excel, CSV, PDF, Text)
- Big Data ecosystem can be Apache Hadoop or Apache Spark
2. Data Scale
- Moving data to a big data ecosystem
- Parallel processing through Apache Spark
- Storing data to NO SQL databases for pre-processing
3. Data Wrangling
- Data Lake development including data pipelines
- Pre-processing tasks including data parsing, data profiling, data shaping, data inferencing, data enrichment, data harmonization, data cleansing, data transformation, data combining
4. Data Context
- Natural Language Processing of un-structured and multi-structured data Entity selection, Featurization, Part-of-Speech, Lemmatization, Stemming, TF- IDF, Scoring - Entity Resolution, Linked Data, Semantic Web, Third Party enrichment, Ontology, knowledge graph, Word Disambiguation
5. Data Exploration
- Exploratory data analysis to summarize dataset characteristics
- Mining the hidden patterns in data
- Statistical analysis concluding right analytical model
6. Data Analytics
- Build rule based analytics model meeting requirements
- Build a salable machine learning model for large data
- Build a deep learning model for complex unstructured data
- Models based on algorithms including hyper-parameter optimization for regression, classification, clustering, association, neural networks, and deep learning - Validating and Testing the data model prepared
7. Data Communication
- Analyzing results via Visualization, D3.JS or Tableau
- Providing actionable insights through automation
SKILLS
1. Framework: Apache Hadoop (MapReduce, YARN, HDFS)
2. Distributed Programming: Apache Spark
- Parallel processing of large scale data, batch or streaming
- In-memory computation of big data in seconds or less
- Machine learning implementation - Graph data analysis
- Real-time streaming - Processing in Python, Scala, and R
3. Machine Learning: Python, PySpark, TensorFlow
4. Key Map Data Mode: Apache HBase, Apache Cassandra
5. Key Value Data: Reddis
6. Graph Database: Apache Giraph, Neo4J
7. Document Data Model: MongoDB
8. Data Ingestion: Apache Flume, Apache Sqoop
9. SQL-like Processing: Apache Hive, Apache Impala
10. Message Oriented Middleware: Apache Kafka, RabbitMQ
11. Service Programming: Apache Avro, Apache Zookeer
12. Scheduling: Apache Oozie
13. Applications: Apache Nutch, Apache Tika
14. Search Engine Framework: Apache Solr, Apache Lucene
15. Data Visualization: D3.JS, Tableau, Qlikview, MicroStrategy
16. Distribution: Cloudera, HortonWorks, MapR, Pivotal
17. Deployment: Amazon AWS, Microsoft Azure, Google Cloud, IBM Bluemix
18. Web: Flask, Django, Angular.JS, Ember.JS, Node.JS
SPECIALITY
1. Data Handling data from marketing automation, CRM, POS, click-through, social media, machines, third party
2. Analytics
- Customer
- Finance
- Supply
- Inventory
- HR
- IoT
3. Application Data Lake, DMP, Dashboards, Apps
Feedback
I worked with 75+ global clients for last 12 years and received an overall 8.75/10 rating (weighted average)
Communication
Possess excellent English skills. Use Skype to communicate with my clients.
Availability
Available from 9:30 AM (IST) to 1:00 AM (IST) for my clients from different time zones.
Contact
Please send your queries to pranav.verma.info[at]gmail.com or Skype at vpranav121
Project history
Pranav had been associated with esteemed businesses including Invitation Homes, Kaiser Permanente, Zurich Financial Services, Hughes Depot Supply, Boeing, Nielsen, Adap TV, Ecolab, CNA Financial, to name very few in list.
Local Availability
Only available in these countries:
India
Available from 9:30 AM (IST) to 1:00 AM (IST) for my clients from different time zones.
I can travel.
I can travel.
Other
Please contact me for work on:
1. Business Intelligence
2. Data Warehousing
3. Data Science
4. Machine Learning
5. Deep Learning
6. Reinforcement Learning
7. Advanced Analytics
8. Statistical Inference
9. Big Data
10. Apache Spark
1. Business Intelligence
2. Data Warehousing
3. Data Science
4. Machine Learning
5. Deep Learning
6. Reinforcement Learning
7. Advanced Analytics
8. Statistical Inference
9. Big Data
10. Apache Spark
