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Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.
AI and Data Science are the hot topics taking over our tech-driven world. It is changing the face of global businesses and raising performance by focusing on data and identifying patterns. With this rising demand, organizations are looking for skilled data scientists who can help convert data into strategic plans for their organizations.
..... Read more42 Hours of Live Instructor-Led Sessions
60 Hours of Assignments and MCQs
36 Hours of Hands-On Practice
6 Real-World Live Projects
Fundamentals to an Advanced Level
Code Reviews by Professionals
Data Science is a discipline growing in demand in cities like Los Angeles. Being among the top jobs in LinkedIn’s Emerging Jobs Report for the last three years, various companies are looking for professionals who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills to enable more data driven decisions.
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Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.
Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.
Get trained by leading practitioners who share best practices from their experience across industries.
Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.
Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!
Six months of post-training mentor guidance to overcome challenges in your Data Science career.
Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.
Lambda function and the object-oriented way of writing classes and objects.
Importing datasets into Python, writing outputs and data analysis using Pandas library.
Data values, data distribution, conditional probability, and hypothesis testing.
Analysis of variance, linear regression, model building, dimensionality reduction techniques.
Evaluation of model parameters, model performance, and classification problems.
Time Series data, its components and tools.
Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.
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Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:
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Hands-on
Learning objectives
In the Probability and Statistics module you will learn:
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Hands-on
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Explore the various approaches to predictive modelling and dive deep into advanced statistics:
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Hands-on
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Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:
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All you need to know to work with time series data with practical case studies and hands-on exercises. You will:
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This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.
Hands-on
The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:
The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.
Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.
In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.
The Data Science with Python course is ideal for:
There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.
To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -
Hardware requirements
Software Requirements
System Requirements
On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.
In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.
By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.
The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.
Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.
The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science.
The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.
Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.
Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years.
Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.
Schedules for our upcoming workshops in Data Science with Python can be found here.
We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.
You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.
Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.
In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.
If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.
Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.
Data Science is a blend of various tools, algorithms, and machine learning principles to analyze and manipulate data with the goal to discover hidden patterns from the raw data. Data Scientist not only does the exploratory analysis to discover insights from data but also uses various advanced machine learning algorithms to predict the future. The main focus is to turn raw data into valuable business information. Data scientists must possess a combination of analytical, statistical, machine learning, data extracting skills, as well as experience with algorithms and coding. In business, these data-driven decisions drawn by data scientists can ultimately lead to increased profitability and improved operational efficiency, workflows, and business performance. In customer-facing organizations, data science helps identify and refine target audiences and in providing better customer experience.
Data Scientists are one of the most promising jobs right now. According to the salaries, opportunities, job openings, etc., the market for Data Science is only going to expand. In Los Angeles, CA, organizations like Amazon Web Services, Sony Pictures Entertainment, UCLA Health, Beyond Limits, Meredith Corporation, Cybernetic Search, Meredith Corporation, AvantStay, Munchkin Inc., NEOGOV, etc. are continuously searching for Data Scientists to help optimize their business process. The reasons for the popularity of Data Science as a career choice are as follows:
Thus, owing to the introduction of new and efficient tools every now and then, it is important to have skilled people to work with these tools. Therefore, there is a huge demand for skilled data scientists. The applications of data science in almost every sector are increasing and are predicted to increase in the future as well. This leads to an increase in the demand and a consequent increase in salary for data scientists.
Besides its financial and economic aspects, data science comes with many exciting aspects. It is a good choice for people who are inquisitive about new things for it has a wide scope for creativity and imagination. It offers a huge space for exploration, and the deeper one dives into it, newer opportunities will unravel for him.
Los Angeles, California is home to Institutes like University of California that offers Master’s in Data Science. This degree can help you understand the basic concepts of Data Science and learn about all the technical skills required to become a Data Scientist. The top skills that are needed to become a data scientist include the following:
1. Basic tools: You must have a knowledge of statistical programming like R, or Python. Solving a problem in data science involves data preprocessing, data preservation, analysis, visualization, and predictions. Python has dedicated libraries such as – Pandas, Numpy, Matplotlib, SciPy, sci-kit-learn, etc. in order to perform these functions. In addition to these, advanced Python libraries such as Tensorflow, Pytorch, and Keras provide Deep Learning tools for Data Scientists. R ideal for not just statistical analysis but also for neural networks. In order to be a proficient Data Scientist, it is necessary to extract and operate on data from the database. Therefore, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax and variety of implementations.
2. Statistics: Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:
3. Software engineering: Data scientists can gain huge benefits by learning concepts from the field of software engineering. It allows them to more easily reutilize their code and algorithms, and share it with collaborators. The important concepts include:
4. Machine Learning: Machine Learning is a subset of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience that is, the data collected over time and analyzed, without being explicitly programmed. It focuses on making future predictions from data available from past experiences. The data scientist feeds good quality data and then train our machines by building machine learning models using the data and different algorithms which depends on what type of data do we have and what kind of task we are trying to automate. Some machine learning methods are as follows:
5. Data Cleaning: Altering and filtering data in such a way that it makes sense is called data cleaning. The general sequential steps to follow are given below:
The tools available to help you with data cleaning are as follows:
6. Data Munging: Data munging, also called data wrangling is the process of mapping the raw data into another format to make it more appropriate and valuable for a variety of downstream purposes such as analytics. The purpose of data wrangling is as follows:
The tools available to perform data munging are as follows:
7. Data visualization: Data visualization methods are an important part of analytics which helps to quickly understand complex data. The process involves the creation of graphical representations of information by utilizing complex sets of numerical or factual figures. The essential data visualization techniques are as follows:
Common types of data visualization are as follows:
8. Unstructured Data: Unstructured data refers to data that does not follow any order like spreadsheet pages, database tables or other linear or ordered data sets and hence does not fit into the row and column structure of a relational database. Non-textual unstructured data such as MP3 audio files, JPEG images, and Flash video files, etc. and textual data including Word documents, PowerPoint presentations, instant messages, collaboration software, documents, books, social media posts, and medical records are all examples of unstructured data. This has led to a demand for new skills to handle such data, like NoSQL. Some of the tools for the analysis of unstructured data are as follows:
Below are the top 5 behavioral traits of a successful Data Scientist -
A data scientist must generate a set of alternative interventions to achieve the end goal. He/She should evaluate the best solutions and implement a plan accordingly.
Los Angeles, CA is the hub for several corporations that have now realized the potential of Data Science and are now searching for data scientists to help them harness this potential. Examples of such organizations include Dataiku, Brillio, Internet Brands, Snapchat, Cybernetic Search, Cedars-Sinai, Zest Finance, Riot Games, Ranker, etc.
A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:
1. Handsome salary: The trend of salaries of data scientists in Los Angeles, CA is: The median salary for a Data Scientist in Los Angeles is $128,189. The average additional cash compensation for a Data Scientist is $16,000 in Los Angeles. The average total compensation for a Data Scientist in Los Angeles, CA is $144,189. Based on your skill set and experience, you can demand higher salaries. The highest paying companies are Facebook, IBM, Accenture, Facebook, Airbnb, and Capital One. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. Data scientists need to have a versatile skill set in their profile as Data Science is a vast domain. Experts have predicted that 40 zettabytes of data will be in existence by 2020.
2. Abundance of positions: There are very few people who have the required skill-set to qualify as a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors. Therefore, Data Science is a vastly abundant field and has a lot of opportunities. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. The various roles offered for a data scientist are as follows:
3. Data Science is versatile: Data Science is a very versatile field with numerous applications in health-care, banking, consultancy services, and e-commerce industries. Data Scientists not only analyze the data but also improve its quality. Data Science deals with enriching data and making it better for their company. They help industries to automate redundant tasks. Companies are using historical data trend patterns to train machines in order to perform repetitive tasks. This makes data science a versatile career option and you get to work on various domains. There are different types of Data Scientists working in different fields:
4. Freedom to work: Data Science is an ever-evolving technology. You are not bound to work for a particular industry. Data Scientist is something that has huge potential. This leads to multiple openings for various roles in all sorts of industries. Recently, the government has also come up with many openings for Data Scientists. You get to learn a lot like technology advances. There is no hard and fast rule to work, no strict path to follow, you must possess the creativity to think beyond the conventional way. You get the freedom to work your own way and put your knowledge in different ways to come up with a solution.
5. Network: Due to the ever-increasing progress in the field of data science, many conferences and workshops are organized around the globe. These workshops invite notable expertise and top leaders from various industries to share their ideas and knowledge with others on the latest and upcoming innovations. Many researchers get to present their work. It provides an opportunity for a data scientist to connect with other people from this field and create a strong network. They can be found hanging out with C-level executives. This helps you to get a referral for better roles that you might be seeking. Also, you get to learn the operating process and work culture in different industries. It opens a door to variations of roles and work. You also get to understand your interests as well.
Below is the list of top business skills needed to become a data scientist:
1. Analytic Problem-Solving – You must be able to apply your problem-solving skills to derive a conclusion. Decision making requires critical thinking and analytical solutions. Problem-solving skills are required for the following:
2. Communication Skills – Data insights are usually presented in the form of tables, charts, or any other concise forms which should be elaborated and explained. Some of the important areas in Data Science where communication skills are important are Storytelling and Data Visualization. Data Scientists need to convey ideas and solutions to the stakeholders. It is important to have good body language and confident skills. A data scientist should have good communication skills to translate the ideas in a way understandable by other people. The various aspects of good communication skills are as follows:
3. Business acumen – The main goal of a data scientist is to translate business problems into data science solutions through the implementation of data science skills. Therefore, it is important to understand the business requirements of the organization you are working with. You must understand how your solutions affect the business on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. This lets you categorize the problems on the grounds of priority.
4. Data inquisitiveness – Curiosity is key towards acquiring mastery of any quantitative field. Data Science requires someone with expertise and knowledge. One must have a curiosity to learn more and experiment with data. A data scientist should always be inquisitive and should know when and where to ask questions. He/ She should always be ready to learn new things and accept challenges. You must update yourself with articles, blogs, new updates in programming languages, tools, etc.
The 5 best ways to brush up your Data Science Skills to get a Data Scientist job are as follows:
Data has become a very important part of our lives. It is generated every day and this has increased the demand of Data Scientists. It is their responsibility to mine the data to gain insights that can help the organization make crucial marketing decisions and optimize their business processes. In Los Angeles, organizations that are currently hiring Data Scientist are Amazon Web Services, Sony Pictures Entertainment, UCLA Health, Beyond Limits, Meredith Corporation, Cybernetic Search, Meredith Corporation, AvantStay, Munchkin Inc., NEOGOV, Dataiku, Brillio, Internet Brands, Snapchat, Cybernetic Search, Cedars-Sinai, Zest Finance, Riot Games, Ranker, etc.
To start working on Data Sets and to practice your Data Science skills, you can take up projects to work on. You can do your hands on in datasets available online. You can categorize the datasets into the following three levels:
Below are the right steps to becoming a successful data scientist:
1. Choose an academic path: A Bachelor’s degree in Computer Science, Statistics, mathematics, etc., or related field is important. More and more data scientists are opting for a master's degree and Ph.D. Higher degrees helps to gain Proficiency in Data Management Technologies. You can go for online courses to add skills to your profile. Some of the certifications available are as follows:
2. Mathematics and statistics: A solid understanding of multivariable calculus and linear algebra is a must for a data scientist. Concepts of probability are the backbone of data science. It forms the foundation of many data analysis techniques. It is important for data scientists to be proficient in math since it simplifies writing algorithms. If you want to become a proficient Data Scientist, then you must be proficient in these topics:
Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:
3. Fundamentals: Before diving into the depths of Data Science, you must start with the fundamental concepts. You must master the basics to build a strong foundation for future learning. Below are some of the ideas that you can incorporate:
4. Specializations: Many data scientists will be heavily specialized in business, often specific segments of the economy or business-related fields like marketing or pricing. The need of the companies varies according to their objectives. You must choose a career path to follow and get proficient in one or more technologies related to data scientists. You can go for certifications, boot camps, and online courses. Having worked on related projects will help you upgrade your profile.
5. Apply for jobs: There are several openings every now and then by companies for various posts related to data science. You need to keep yourself updated on these notifications and prepare yourselves for the interviews.
Los Angeles, CA is home to many leading institutes.A degree from University of Southern California and the University of California can help you get expertise in technical skills of Data Science. Also, it will help improve your CV.
About 88% of data scientists have a Master's degree while about 46% have a Ph.D. degree. A degree is very important because of the following –
Los Angeles, CA is home to the University of Southern California and is renowned for its data science degree. A Master’s degree is not necessary to become a Data Scientist. But if you possess it, it is an added benefit and increases your chances of getting hired. The advantages of having a Master’s degree are as follows:
Knowledge of programming is perhaps the most important and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include the following:
Data sets are basically a collection of data. Algorithms are written to work on these data sets, therefore it is very essential to have a command over one or more programming languages.
It is heavily preferred in several data science projects for processing of large data sets. Tools are evolving for pulling information out of Hadoop clusters:
The average income of Data Scientist in Los Angeles is $98,294.
As compared to the average salary of a Data Scientist of $100,450 in New Jersey, the salary in Los Angeles is $2,156 less.
In Los Angeles, the average salary of a data scientist is $98,294 as compared to $110,925 in Chicago.
The average income of a data scientist in Los Angeles is $98,294 as compared to $125,310 in Boston.
In California, cities like San Francisco and San Diego have an average pay of $119,953 and $97,183 respectively for data scientists.
The Data Scientists in California are in high demand right now.
Here are the benefits of being a Data Scientist in Los Angeles:
Being a Data Scientist in Los Angeles offers several perks and advantages. There are opportunities to connect with different people in various conferences, summit, and meetups. Data Scientists play a major role in gathering useful insights after analyzing the raw data. This puts them in connection with top-level executives. Also, they have the luxury to work in the field that a person is interested in.
In Los Angeles, companies hiring Data Scientists include Snap Inc. and Capital Group.
| S.No | Conference name | Date | Venue |
| 1. | Data Science Salon, Los Angeles | November 7, 2019 | Red Bull Media House |
| 2. | Data Con LA | August 17, 2019 | The University of Southern California |
| 3. | IDEAS SoCal AI & Data Science Conference 2019, Los Angeles | Sat, October 26, 2019 | Los Angeles Convention Center |
| 4. | SatRday LA – R Conference | April 6, 2019 | UCLA James West Alumni Center |
| 5. | Microsoft Reporting & Analytics by Ted Stathakis (#SQLSatLA) | Friday, June 14, 2019 | Loyola Marymount University (LMU) in Playa Vista |
| 6. | ACM IUI 2019, Los Angeles | March 16 to March 20, 2019 | Marriott Marina Del Re |
| 7. | IEEE BigData 2019: IEEE International Conference on Big Data, Los Angeles, CA, USA | Dec 9, 2019 - Dec 12, 2019 | Los Angeles, CA, USA |
| 8. | John Langford @ ZEFR | Saturday, June 15, 2019 | ZEFR, 4101 Redwood Ave · Marina Del Rey, ca |
| 9. | Using Apache Cassandra and Apache Kafka to Scale Next Gen Applications | Thursday, May 9, 2019 | Verizon Digital Media Service |
| 10. | ROpenSci Unconference, Los Angeles (USA) | May 25, 2019-26, 2019 | Los Angeles (USA) |
1. Data Science Salon, Los Angeles
2. Data Con LA, Los Angeles
3. IDEAS SoCal AI & Data Science Conference 2019, Los Angeles
4. SatRday LA – R Conference, Los Angeles
5. Microsoft Reporting & Analytics by Ted Stathakis (#SQLSatLA), Los Angeles
6. ACM IUI 2019, Los Angeles
7. IEEE BigData 2019: IEEE International Conference on Big Data, Los Angeles
8. John Langford @ ZEFR, Los Angeles
9. Using Apache Cassandra and Apache Kafka to Scale Next Gen Applications, Los Angeles
10. ROpenSci Unconference, Los Angeles (USA)
| S.No | Conference name | Date | Venue |
| 1. | XLIVE Data & Analytics Summit, executive-level forum across music, entertainment, sports, and culinary industries | 4 April 2017, - 5 April, 2017 | |
| 2. | Southern California Data Science Conference 2017 | October 22, 2017 | Visit Pasadena, 300 E Green St, Pasadena, CA 91101, USA |
| 3. | Data Science SALON | 14 December, 2017 | |
| 4. | XLIVE Data & Analytics Summit | 3 April, 2018 - 4 April, 2018 | Hudson Loft, 1200, S Hope St, Los Angeles, CA 90015 |
| 5. | Big Data Day LA | 11 August, 2018 | 3670 Trousdale Pkwy, Los, Angeles, CA 90089, United States |
| 6. | Data Science Salon | 13 September, 2018 | Red Bull Media House, 1740 Stewart St, Santa Monica, CA 90404 |
| 7. | Converge: The Intersection of Data Science, Market Research & Analytics | 4 - 5 December, 2018 | The Westin Bonaventure Hotel & Suites, 404, S. Figueroa Street, Los Angeles, CA 90071 |
1. XLIVE Data & Analytics Summit, executive-level forum across music, entertainment, sports, and culinary industries, Los Angeles
2. Southern California Data Science Conference 2017, Los Angeles
3. Data Science SALON, Los Angeles
4. XLIVE Data & Analytics Summit, Los Angeles
5. Big Data Day LA, Los Angeles
6. Data Science Salon, Los Angeles
7. Converge: The Intersection of Data Science, Market Research & Analytics, Los Angeles
Here is the logical sequence of steps you should follow to get a job as a Data Scientist.
Follow the below steps to increase your chances of success if you are an aspiring data scientist:
A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs.
In this modern business scenario that is generating tons of data every day, the role of a Data Scientist is becoming all the more important. This is because the data generated is a gold mine of patterns and ideas that could prove to be very helpful in the advancement of a business. It is up to the data scientist to extract the relevant information and make sense of it in order to benefit the business.
Data Scientist Roles & Responsibilities:
The average annual salary of a Data Scientist in Los Angeles, CA is $128,189. A Data analyst earns upto $62,088 per year while a database administrator makes about $73,622 per year.
The career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: A business intelligence analyst is responsible for analyzing data that is used by a business or organization that supports it in decision making. They perform tasks such as defining, reporting on or otherwise developing new structures for business intelligence to serve a specific purpose. Report writing can be a vital element for this role. They ensure that the business is always in the most favorable position by comparing data to competitors and observing industrial trends then creating reports and communicating the same to the organization.
Data Mining Engineer: Data Mining Engineer creates and boosts statistical and predictive models and algorithms to vast data sets. They will be working as an engineer to productize, facilitate and implement the systems needed for the analysis and must be able to explain and present hypotheses and analysis results to a wide audience in a clear and concise manner.
A Data Mining Engineer has underlined functions:
Data Architect: A data architect builds and maintains a company’s database by identifying structural and installation solution.
Data Scientist: The data scientist is an analytical data expert having the technical skills required to solve complex problems. Data scientists are responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy. The various roles include the following:
Chief Data Officer: The chief data officer is a senior executive who surveys a wide range of data related functions through data processing, data mining, analysis and other measures for entity-wide governance and utilization information as a resource.
Below are the top professional organizations for data scientists in Los Angeles, CA –
Referrals are the most effective way to get hired. Some of the other ways to network with data scientists in Los Angeles, CA are:
There are several career options for a data scientist –
We have compiled the key points, which the employers generally look for while hiring data scientists:
As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.
Follow these steps to successfully install Python 3 on windows:
Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:
python --version
python -m pip install -U pip
Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.
To install python 3 on Mac OS X, just follow the below steps:
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
export PATH="/usr/local/opt/python/libexec/bin:$PATH"
$ brew install python
About Los Angeles
The land of dreams – that's LA for you. This is the land of Hollywood, of pristine white beaches, beautiful people, rock stars, great writers, directors, and actors. While entertainment is a huge economic driver, the city has seen exceptional growth in international trade, aerospace, technology, petroleum, retail, and real estate sectors too. The city is also home to the University of Southern California (USC) which attracts students from all over the world.
Data Science with Python Training at KnowledgeHut
Data Science with Python opens your career into one of the most promising fields combined with the knowledge of using the fastest growing programming language. KnowledgeHut also helps you offering a variety of courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others.
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