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As any puzzle-solver, a data analyst needs the following soft skills: <\/div>

<\/div>
  1. Attention to detail<\/li>
  2. A methodical and logical approach<\/li>
  3. Problem-solving skills<\/li>
  4. Strong business acumen<\/li>
  5. Presentation skills<\/li>
  6. Ability to work in a team<\/li><\/ol>

    <\/div>
    Guy Tsror, Data Scientist, Local Logic recently shared his experience working in the field during a webinar. 

    <\/div>
    \u201cA data analyst position implies analyzing and making sense of big sets of data to answer questions your clients have.\u201d <\/blockquote>

    <\/div>
    As for the hard skills, a data analyst has to have a good knowledge of: <\/div>

    <\/div>
    1. Excel<\/li>
    2. Data Visualization<\/li>
    3. Statistics and SQL<\/li>
    4. Programming: Python, HTML, JavaScript, CSS, and (maybe) R.<\/li><\/ol>

      <\/div>
      If you have a degree related to computer science, mathematics, statistics, or economics, a few of these might sound familiar. If not, set up a call with Camille<\/a> to see how you can learn them in 9 short weeks!<\/div>

      <\/div>

      A Data Analyst\u2019s typical day<\/h2>

      <\/div>
      A typical project for a data analyst involves gathering data, spotting patterns, and drawing conclusions about how to move forward based on these findings. <\/div>

      <\/div>
      How to accomplish this? Sciencesoft <\/a>categorizes data analytics into 4 types - <\/div>

      <\/div>
      1. Descriptive analytics - \u201cwhat happened?\u201d<\/em><\/li>
      2. Diagnostic analytics - \u201cwhy did it happen?\u201d<\/em><\/li>
      3. Predictive analytics - \u201cwhat is most likely to happen?\u201d<\/em><\/li>
      4. Prescriptive analytics - \u201chow should we proceed?\u201d<\/em><\/li><\/ol>

        <\/div>
        A data analyst<\/a> will work with a team to decide how best to answer the above questions from data cleansing through data processing and the final report. This process typically plays out like this:<\/div>

        <\/div>
        Gathering data first requires data cleansing, which entails removing irrelevant and corrupted information to filter through the raw information. Teams then use descriptive statistics to understand the cleaned data set. <\/div>

        <\/div>
        \u201cThe capability of working in a team, sharing your code, getting code reviews is really important. Furthemore, understanding who your client is is crucial. It will affect how you handle or share the data and how you communicate the results of your analysis.\u201d Guy T.<\/blockquote>

        <\/div>

        How about a Data Scientist\u2019s role ?<\/h2>

        <\/div>
        \u201cMy favorite thing about being a Data scientist is that it really touches a lot of different aspects. You have to do subject matter research, define which questions are the right ones to ask, which data can answer your question and how to handle and process that data, how to make it usable for your models and be able to measure the model success.\u201d Guy T.<\/em><\/div>

        <\/div>
        Data scientists<\/a> use data analysts findings to dabble in inferential statistics, which make use of probability theory to predict future results from discovered trends by creating their own algorithms*. The findings from these processes are then communicated with the management team through data visualization, who collaborate to create an action plan. <\/div>

        <\/div>

        Where do data specialists work?<\/h2>

        <\/div>
        The beauty of data is that it\u2019s everywhere. Banks, universities, marketing agencies, public sector organizations, software development companies all use data analysts to determine how best to respond to consumer needs and adapt strategy.<\/div>

        <\/div>
        \u201cIt\u2019s a great time to work in the field. There are a bunch of opportunities in so many industries. With the covid reality, you might even find work opportunities outside Canada while living in Montreal. There are also a lot of people looking for these positions so when you apply, it\u2019s important to think about how you are standing out from others.\u201d <\/em>Guy T.<\/div>

        <\/div>
        With available data-related jobs exponentially increasing<\/a> and a recent World Economic Forum report <\/a>predicting data analysts & data scientists as the #1 emerging job for 2022, data science is an extremely lucrative and in-demand field. <\/div>

        <\/div>
        Montreal is a globally recognized center for AI<\/a> and all things data. If you are interested in acquiring the skills needed to break into this field, reach out to Camille about joining our Data Science Bootcamp <\/a>on October 19!<\/div>

        <\/div>
        *Data analysts often move on to become data scientists, although an advanced technical degree is usually required.<\/em><\/div>

        <\/div>","_contenu_de_larticle":null,"titre_de_la_banniere":null,"_titre_de_la_banniere":null,"paragraphe_de_la_banniere":null,"_paragraphe_de_la_banniere":null,"intitule_du_bouton_de_la_banniere":null,"_intitule_du_bouton_de_la_banniere":null,"lien_du_bouton_de_la_banniere":null,"_lien_du_bouton_de_la_banniere":null,"yoast_head":"\nJoining a data team: Data Analyst and Data Scientist roles | Le Wagon<\/title>\n<meta name=\"description\" content=\"Imagine data as a puzzle. 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